1
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Karras F, Kunz M. Patient-derived melanoma models. Pathol Res Pract 2024; 259:155231. [PMID: 38508996 DOI: 10.1016/j.prp.2024.155231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 03/22/2024]
Abstract
Melanoma is a very aggressive, rapidly metastasizing tumor that has been studied intensively in the past regarding the underlying genetic and molecular mechanisms. More recently developed treatment modalities have improved response rates and overall survival of patients. However, the majority of patients suffer from secondary treatment resistance, which requires in depth analyses of the underlying mechanisms. Here, melanoma models based on patients-derived material may play an important role. Consequently, a plethora of different experimental techniques have been developed in the past years. Among these are 3D and 4D culture techniques, organotypic skin reconstructs, melanoma-on-chip models and patient-derived xenografts, Every technique has its own strengths but also weaknesses regarding throughput, reproducibility, and reflection of the human situation. Here, we provide a comprehensive overview of currently used techniques and discuss their use in different experimental settings.
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Affiliation(s)
- Franziska Karras
- Institute of Pathology, Otto-von-Guericke University Magdeburg, Leipziger Str. 44, Magdeburg 39120, Germany.
| | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University Medical Center Leipzig, Philipp-Rosenthal-Str. 23, Leipzig 04103, Germany
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2
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Ladabaum U. Of Humans and Machines in Endoscopy: Flying Solo, Instrument Aided, or on Autopilot? Gastroenterology 2024; 167:210-212. [PMID: 38548191 DOI: 10.1053/j.gastro.2024.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/21/2024]
Affiliation(s)
- Uri Ladabaum
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, California.
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3
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Maurya A, Stanley RJ, Aradhyula HY, Lama N, Nambisan AK, Patel G, Saeed D, Swinfard S, Smith C, Jagannathan S, Hagerty JR, Stoecker WV. Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1137-1150. [PMID: 38332404 PMCID: PMC11169204 DOI: 10.1007/s10278-024-00969-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/22/2023] [Accepted: 10/27/2023] [Indexed: 02/10/2024]
Abstract
In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explainability. To dermatologists, the presence of telangiectasia, or narrow blood vessels that typically appear serpiginous or arborizing, is a critical indicator of basal cell carcinoma (BCC). Exploiting the feature information present in telangiectasia through a combination of DL-based techniques could create a pathway for both, improving DL results as well as aiding dermatologists in BCC diagnosis. This study demonstrates a novel "fusion" technique for BCC vs non-BCC classification using ensemble learning on a combination of (a) handcrafted features from semantically segmented telangiectasia (U-Net-based) and (b) deep learning features generated from whole lesion images (EfficientNet-B5-based). This fusion method achieves a binary classification accuracy of 97.2%, with a 1.3% improvement over the corresponding DL-only model, on a holdout test set of 395 images. An increase of 3.7% in sensitivity, 1.5% in specificity, and 1.5% in precision along with an AUC of 0.99 was also achieved. Metric improvements were demonstrated in three stages: (1) the addition of handcrafted telangiectasia features to deep learning features, (2) including areas near telangiectasia (surround areas), (3) discarding the noisy lower-importance features through feature importance. Another novel approach to feature finding with weak annotations through the examination of the surrounding areas of telangiectasia is offered in this study. The experimental results show state-of-the-art accuracy and precision in the diagnosis of BCC, compared to three benchmark techniques. Further exploration of deep learning techniques for individual dermoscopy feature detection is warranted.
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Affiliation(s)
- Akanksha Maurya
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | - R Joe Stanley
- Missouri University of Science &Technology, Rolla, MO, 65209, USA.
| | | | - Norsang Lama
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | - Anand K Nambisan
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | | | | | | | - Colin Smith
- A.T. Still University of Health Sciences, Kirksville, MO, USA
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4
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Franklin G, Stephens R, Piracha M, Tiosano S, Lehouillier F, Koppel R, Elkin PL. The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective. Life (Basel) 2024; 14:652. [PMID: 38929638 PMCID: PMC11204917 DOI: 10.3390/life14060652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 06/28/2024] Open
Abstract
Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.
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Affiliation(s)
- Gillian Franklin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
| | - Rachel Stephens
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Muhammad Piracha
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Shmuel Tiosano
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
| | - Frank Lehouillier
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
| | - Ross Koppel
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Institute for Biomedical Informatics, Perelman School of Medicine, and Sociology Department, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Peter L. Elkin
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14203, USA; (G.F.); (R.S.); (M.P.); (F.L.); (R.K.)
- Department of Veterans Affairs, Knowledge Based Systems and Western New York, Veterans Affairs, Buffalo, NY 14215, USA
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5
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Salinas MP, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, Rotemberg V, Briones J, Mery D, Navarrete-Dechent C. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. NPJ Digit Med 2024; 7:125. [PMID: 38744955 PMCID: PMC11094047 DOI: 10.1038/s41746-024-01103-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/04/2024] [Indexed: 05/16/2024] Open
Abstract
Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.
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Affiliation(s)
- Maria Paz Salinas
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javiera Sepúlveda
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Leonel Hidalgo
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominga Peirano
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Morel
- Universidad Catolica-Evidence Center, Cochrane Chile Associated Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pablo Uribe
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
- Melanoma and Skin Cancer Unit, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Veronica Rotemberg
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Juan Briones
- Department of Oncology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Domingo Mery
- Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Navarrete-Dechent
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Melanoma and Skin Cancer Unit, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Ingvar Å, Oloruntoba A, Sashindranath M, Miller R, Soyer HP, Guitera P, Caccetta T, Shumack S, Abbott L, Arnold C, Lawn C, Button-Sloan A, Janda M, Mar V. Minimum labelling requirements for dermatology artificial intelligence-based Software as Medical Device (SaMD): A consensus statement. Australas J Dermatol 2024; 65:e21-e29. [PMID: 38419186 DOI: 10.1111/ajd.14222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/21/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.
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Affiliation(s)
- Åsa Ingvar
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Dermatology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | | | - Maithili Sashindranath
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Robert Miller
- Australasian College of Dermatologists, Sydney, Australia
| | - H Peter Soyer
- Australasian College of Dermatologists, Sydney, Australia
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Pascale Guitera
- Australasian College of Dermatologists, Sydney, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, Victoria, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Tony Caccetta
- Australasian College of Dermatologists, Sydney, Australia
- Perth Dermatology Clinic, Perth, Western Australia, Australia
| | - Stephen Shumack
- Australasian College of Dermatologists, Sydney, Australia
- Royal North Shore Hospital of Sydney, Sydney, New South Wales, Australia
| | - Lisa Abbott
- Australasian College of Dermatologists, Sydney, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- The Skin Hospital, Sydney, New South Wales, Australia
| | - Chris Arnold
- BioGrid Australia Ltd, Melbourne, Australia
- Hodgson Associates, Melbourne, Australia
- Australasian Society of Cosmetic Dermatologists, Melbourne, Australia
| | - Craig Lawn
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
- Centre of Excellence in Melanoma Imaging, Brisbane, Queensland, Australia
| | | | - Monika Janda
- Australasian College of Dermatologists, Sydney, Australia
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia
| | - Victoria Mar
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Australasian College of Dermatologists, Sydney, Australia
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7
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Goessinger EV, Cerminara SE, Mueller AM, Gottfrois P, Huber S, Amaral M, Wenz F, Kostner L, Weiss L, Kunz M, Maul JT, Wespi S, Broman E, Kaufmann S, Patpanathapillai V, Treyer I, Navarini AA, Maul LV. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence. J Eur Acad Dermatol Venereol 2024; 38:945-953. [PMID: 38158385 DOI: 10.1111/jdv.19777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 10/23/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations affect CNN robustness. OBJECTIVES To investigate the consistency of melanoma risk assessment by two commercially available CNNs to help formulate recommendations for current clinical use. METHODS A comparative cohort study was conducted from January to July 2022 at the Department of Dermatology, University Hospital Basel. Five dermoscopic images of 116 different lesions on the torso of 66 patients were captured consecutively by the same operator without deliberate rotation. Classification was performed by two CNNs (CNN-1/CNN-2). Lesions were divided into four subgroups based on their initial risk scoring and clinical dignity assessment. Reliability was assessed by variation and intraclass correlation coefficients. Excisions were performed for melanoma suspicion or two consecutively elevated CNN risk scores, and benign lesions were confirmed by expert consensus (n = 3). RESULTS 117 repeated image series of 116 melanocytic lesions (2 melanomas, 16 dysplastic naevi, 29 naevi, 1 solar lentigo, 1 suspicious and 67 benign) were classified. CNN-1 demonstrated superior measurement repeatability for clinically benign lesions with an initial malignant risk score (mean variation coefficient (mvc): CNN-1: 49.5(±34.3)%; CNN-2: 71.4(±22.5)%; p = 0.03), while CNN-2 outperformed for clinically benign lesions with benign scoring (mvc: CNN-1: 49.7(±22.7)%; CNN-2: 23.8(±29.3)%; p = 0.002). Both systems exhibited lowest score consistency for lesions with an initial malignant risk score and benign assessment. In this context, averaging three initial risk scores achieved highest sensitivity of dignity assessment (CNN-1: 94%; CNN-2: 89%). Intraclass correlation coefficients indicated 'moderate'-to-'good' reliability for both systems (CNN-1: 0.80, 95% CI:0.71-0.87, p < 0.001; CNN-2: 0.67, 95% CI:0.55-0.77, p < 0.001). CONCLUSIONS Potential user-induced image changes can significantly influence CNN classification. For clinical application, we recommend using the average of three initial risk scores. Furthermore, we advocate for CNN robustness optimization by cross-validation with repeated image sets. TRIAL REGISTRATION ClinicalTrials.gov (NCT04605822).
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Affiliation(s)
- E V Goessinger
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - S E Cerminara
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - A M Mueller
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - P Gottfrois
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - S Huber
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - M Amaral
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - F Wenz
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - L Kostner
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - L Weiss
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - M Kunz
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - J-T Maul
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - S Wespi
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - E Broman
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - S Kaufmann
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - V Patpanathapillai
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - I Treyer
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - A A Navarini
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - L V Maul
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
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8
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Winkler JK, Kommoss KS, Toberer F, Enk A, Maul LV, Navarini AA, Hudson J, Salerni G, Rosenberger A, Haenssle HA. Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance. Eur J Cancer 2024; 202:114026. [PMID: 38547776 DOI: 10.1016/j.ejca.2024.114026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 04/21/2024]
Abstract
IMPORTANCE Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. DESIGN AND PATIENTS In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations. RESULTS At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). CONCLUSIONS ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.
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Affiliation(s)
- Julia K Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
| | | | - Ferdinand Toberer
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Alexander Enk
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Lara V Maul
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | | | - Jeremy Hudson
- North Queensland Skin Centre, Townsville, Queensland, Australia
| | - Gabriel Salerni
- Department of Dermatology, Hospital Provincial del Centenario de Rosario- Universidad Nacional de Rosario, Rosario, Argentina
| | - Albert Rosenberger
- Institute of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
| | - Holger A Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
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9
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Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, Lewis JM, Gabr AE, Smiley A, Tiwari RK, Etienne M. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel) 2024; 14:557. [PMID: 38792579 PMCID: PMC11122160 DOI: 10.3390/life14050557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.
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Affiliation(s)
- Rahim Hirani
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Kaleb Noruzi
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Hassan Khuram
- College of Medicine, Drexel University, Philadelphia, PA 19129, USA
| | - Anum S. Hussaini
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Esewi Iyobosa Aifuwa
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Kencie E. Ely
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89106, USA
| | - Joshua M. Lewis
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Ahmed E. Gabr
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Abbas Smiley
- School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - Raj K. Tiwari
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Mill Etienne
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Department of Neurology, New York Medical College, Valhalla, NY 10595, USA
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10
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Krakowski I, Kim J, Cai ZR, Daneshjou R, Lapins J, Eriksson H, Lykou A, Linos E. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:78. [PMID: 38594408 PMCID: PMC11004168 DOI: 10.1038/s41746-024-01031-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/05/2024] [Indexed: 04/11/2024] Open
Abstract
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
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Affiliation(s)
- Isabelle Krakowski
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Jiyeong Kim
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Zhuo Ran Cai
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Roxana Daneshjou
- Department of Dermatology, Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Jan Lapins
- Department of Dermatology, Theme Inflammation, Karolinska University Hospital, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Unit of Head-Neck-, Lung- and Skin Cancer, Skin Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Anastasia Lykou
- Department of Education, University of Nicosia, Nicosia, Cyprus
| | - Eleni Linos
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA.
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11
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Miller I, Rosic N, Stapelberg M, Hudson J, Coxon P, Furness J, Walsh J, Climstein M. Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review. Cancers (Basel) 2024; 16:1443. [PMID: 38611119 PMCID: PMC11011068 DOI: 10.3390/cancers16071443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. METHODS A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. RESULTS A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. CONCLUSION Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians' work is supported by AI, facilitating management decisions and improving health outcomes.
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Affiliation(s)
- Ian Miller
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Nedeljka Rosic
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
| | - Michael Stapelberg
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Specialist Suite, John Flynn Hospital, Tugun, QLD 4224, Australia
| | - Jeremy Hudson
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - Paul Coxon
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- North Queensland Skin Centre, Townsville, QLD 4810, Australia
| | - James Furness
- Water Based Research Unit, Bond University, Robina, QLD 4226, Australia;
| | - Joe Walsh
- Sport Science Institute, Sydney, NSW 2000, Australia;
- AI Consulting Group, Sydney, NSW 2000, Australia
| | - Mike Climstein
- Aquatic Based Research, Southern Cross University, Bilinga, QLD 4225, Australia; (I.M.); (N.R.)
- Faculty of Health, Southern Cross University, Bilinga, QLD 4225, Australia (P.C.)
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, NSW 2050, Australia
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12
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Wei ML, Tada M, So A, Torres R. Artificial intelligence and skin cancer. Front Med (Lausanne) 2024; 11:1331895. [PMID: 38566925 PMCID: PMC10985205 DOI: 10.3389/fmed.2024.1331895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.
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Affiliation(s)
- Maria L. Wei
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
| | - Mikio Tada
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, United States
| | - Alexandra So
- School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Rodrigo Torres
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
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13
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Haggenmüller S, Schmitt M, Krieghoff-Henning E, Hekler A, Maron RC, Wies C, Utikal JS, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A, French LE, Heinzerling L, Schlager JG, Ghoreschi K, Schlaak M, Hilke FJ, Poch G, Korsing S, Berking C, Heppt MV, Erdmann M, Haferkamp S, Drexler K, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather JN, Fröhling S, Brinker TJ. Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics. JAMA Dermatol 2024; 160:303-311. [PMID: 38324293 PMCID: PMC10851139 DOI: 10.1001/jamadermatol.2023.5550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/01/2023] [Indexed: 02/08/2024]
Abstract
Importance The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals. Objective To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics. Design, Setting, and Participants This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological whole-slide images prospectively acquired at 6 German university hospitals between April 2021 and February 2023 and benchmarked it using both a holdout and an external test dataset. Data analysis was performed from February to April 2023. Exposures All whole-slide images were retrospectively analyzed by an AI-based classifier without influencing routine clinical care. Main Outcomes and Measures The area under the receiver operating characteristic curve (AUROC) served as the primary end point for evaluating the diagnostic performance. Secondary end points included balanced accuracy, sensitivity, and specificity. Results The study included 1025 whole-slide images of clinically melanoma-suspicious skin lesions from 923 patients, consisting of 388 histopathologically confirmed invasive melanomas and 637 nevi. The median (range) age at diagnosis was 58 (18-95) years for the training set, 57 (18-93) years for the holdout test dataset, and 61 (18-95) years for the external test dataset; the median (range) Breslow thickness was 0.70 (0.10-34.00) mm, 0.70 (0.20-14.40) mm, and 0.80 (0.30-20.00) mm, respectively. The federated approach (0.8579; 95% CI, 0.7693-0.9299) performed significantly worse than the classical centralized approach (0.9024; 95% CI, 0.8379-0.9565) in terms of AUROC on a holdout test dataset (pairwise Wilcoxon signed-rank, P < .001) but performed significantly better (0.9126; 95% CI, 0.8810-0.9412) than the classical centralized approach (0.9045; 95% CI, 0.8701-0.9331) on an external test dataset (pairwise Wilcoxon signed-rank, P < .001). Notably, the federated approach performed significantly worse than the ensemble approach on both the holdout (0.8867; 95% CI, 0.8103-0.9481) and external test dataset (0.9227; 95% CI, 0.8941-0.9479). Conclusions and Relevance The findings of this diagnostic study suggest that federated learning is a viable approach for the binary classification of invasive melanomas and nevi on a clinically representative distributed dataset. Federated learning can improve privacy protection in AI-based melanoma diagnostics while simultaneously promoting collaboration across institutions and countries. Moreover, it may have the potential to be extended to other image classification tasks in digital cancer histopathology and beyond.
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Affiliation(s)
- Sarah Haggenmüller
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Max Schmitt
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Achim Hekler
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roman C. Maron
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christoph Wies
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jochen S. Utikal
- Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karls University of Heidelberg, Mannheim, Germany
- Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany
- DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
| | - Friedegund Meier
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Sarah Hobelsberger
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Frank F. Gellrich
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mildred Sergon
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Axel Hauschild
- Department of Dermatology, University Hospital (UKSH), Kiel, Germany
| | - Lars E. French
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
- Dr Phillip Frost Department of Dermatology and Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, Florida
| | - Lucie Heinzerling
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Justin G. Schlager
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
| | - Kamran Ghoreschi
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Max Schlaak
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Franz J. Hilke
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gabriela Poch
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sören Korsing
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Carola Berking
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Markus V. Heppt
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Sebastian Haferkamp
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Konstantin Drexler
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Dirk Schadendorf
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Wiebke Sondermann
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Matthias Goebeler
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg and National Center for Tumor Diseases (NCT) WERA, Würzburg, Germany
| | - Bastian Schilling
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg and National Center for Tumor Diseases (NCT) WERA, Würzburg, Germany
| | - Jakob N. Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J. Brinker
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
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14
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Brancaccio G, Balato A, Malvehy J, Puig S, Argenziano G, Kittler H. Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check. J Invest Dermatol 2024; 144:492-499. [PMID: 37978982 DOI: 10.1016/j.jid.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/08/2023] [Accepted: 10/01/2023] [Indexed: 11/19/2023]
Abstract
The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.
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Affiliation(s)
| | - Anna Balato
- Dermatology Unit, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | | | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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15
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Giulini M, Goldust M, Grabbe S, Ludwigs C, Seliger D, Karagaiah P, Schepler H, Butsch F, Weidenthaler-Barth B, Rietz S. Combining artificial intelligence and human expertise for more accurate dermoscopic melanoma diagnosis: A 2-session retrospective reader study. J Am Acad Dermatol 2024:S0190-9622(24)00307-4. [PMID: 38430100 DOI: 10.1016/j.jaad.2023.12.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 12/27/2023] [Accepted: 12/31/2023] [Indexed: 03/03/2024]
Affiliation(s)
- Mario Giulini
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
| | - Mohamad Goldust
- Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut
| | - Stephan Grabbe
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany.
| | | | | | - Priyanka Karagaiah
- Department of Dermatology, Bangalore Medical College and Research Institute, Bengaluru, India
| | - Hadrian Schepler
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
| | - Florian Butsch
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
| | | | - Stephan Rietz
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
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16
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Goessinger EV, Niederfeilner JC, Cerminara S, Maul JT, Kostner L, Kunz M, Huber S, Koral E, Habermacher L, Sabato G, Tadic A, Zimmermann C, Navarini A, Maul LV. Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study. J Eur Acad Dermatol Venereol 2024. [PMID: 38411348 DOI: 10.1111/jdv.19905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/22/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting. OBJECTIVES To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence. METHODS A prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high-risk of developing melanoma, with resected or advanced disease) and 8 dermatologists. Patients underwent skin cancer screening by a dermatologist with subsequent 2D and 3D total-body photography (TBP). Any suspicious and all melanocytic skin lesions ≥3 mm were imaged with digital dermoscopes and classified by corresponding convolutional neural networks (CNNs). Excisions were performed based on dermatologist's melanoma suspicion, study-defined elevated CNN risk-scores and/or melanoma suspicion by AuI. Subsequently, all patients and dermatologists were surveyed about their experience using questionnaires, including quantification of patient's safety sense following different examinations (subjective safety score (SSS): 0-10). RESULTS Most patients believed AI could improve diagnostic performance (95.5%, n = 192/201). In total, 83.4% preferred AuI-based skin cancer screening compared to examination by AI or dermatologist alone (3D-TBP: 61.3%; 2D-TBP: 22.1%, n = 199). Regarding SSS, AuI induced a significantly higher feeling of safety than AI (mean-SSS (mSSS): 9.5 vs. 7.7, p < 0.0001) or dermatologist screening alone (mSSS: 9.5 vs. 9.1, p = 0.001). Most dermatologists expressed high trust in AI examination results (3D-TBP: 90.2%; 2D-TBP: 96.1%, n = 205). In 68.3% of the examinations, dermatologists felt that diagnostic accuracy improved through additional AI-assessment (n = 140/205). Especially beginners (<2 years' dermoscopic experience; 61.8%, n = 94/152) felt AI facilitated their clinical work compared to experts (>5 years' dermoscopic experience; 20.9%, n = 9/43). Contrarily, in divergent risk assessments, only 1.5% of dermatologists trusted a benign CNN-classification more than personal malignancy suspicion (n = 3/205). CONCLUSIONS While patients already prefer AuI with 3D-TBP for melanoma recognition, dermatologists continue to rely largely on their own decision-making despite high confidence in AI-results. TRIAL REGISTRATION ClinicalTrials.gov (NCT04605822).
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Affiliation(s)
- Elisabeth Victoria Goessinger
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | | | - Sara Cerminara
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Julia-Tatjana Maul
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Lisa Kostner
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Michael Kunz
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Stephanie Huber
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - Emrah Koral
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - Lea Habermacher
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Gianna Sabato
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Andrea Tadic
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | | | - Alexander Navarini
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Lara Valeska Maul
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
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17
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Augustin M, Reinders P, Janke TM, Strömer K, von Kiedrowski R, Kirsten N, Zink A, Otten M. Attitudes Toward and Use of eHealth Technologies Among German Dermatologists: Repeated Cross-Sectional Survey in 2019 and 2021. J Med Internet Res 2024; 26:e45817. [PMID: 38345855 PMCID: PMC10897787 DOI: 10.2196/45817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 06/29/2023] [Accepted: 10/17/2023] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND In recent years, legal and infrastructural conditions have been set to improve the adoption of digital applications in health care in Germany. The impact of these actions was amplified by the COVID-19 pandemic. So far, no studies have confirmed this progress in dermatology. OBJECTIVE The aim of this study was to measure changes in knowledge, interest, expectation, and use of digital applications in health care among dermatologists in Germany in 2019 and 2021. METHODS We administered a repeated cross-sectional survey among dermatologists in medical practices and clinics in Germany at 2 time points: t1 (2019; before the COVID-19 pandemic) and t2 (2021; during the COVID-19 pandemic). We used a standardized questionnaire, including items on respondents' knowledge, interest, expectation, and use of digital applications, as well as their demographics. The survey was distributed by post and email. The data were analyzed descriptively as well as with multiple logistic regressions. RESULTS At t1, 585 (272/571, 47.6% female; mean age 52.4, SD 8.9 years) dermatologists and at t2, 792 (360/736, 48.9% female; mean age 54.3, SD 8.6 years) dermatologists participated in this survey. Interest in digital medicine was higher at t1 than at t2 (381/585, 65.1% vs 458/792, 57.8%; P≤.001). Nevertheless, 38.6% (306/792) had used digital applications more often since the beginning of the pandemic. For example, real-time telemedicine with patients (12/585, 2.1% vs 160/792, 7.6%; P≤.001) and other specialists did increase (33/385, 5.7% vs 181/792, 22.8%; P≤.001). Almost one-third expressed great concerns about digitalization (272/792, 34.3% vs 294/792, 37.1%; P=.21). Spatial analysis revealed higher interest in, more positive expectations toward, and higher use of digital applications in urban areas in comparison to rural areas. For instance, dermatologists from urban areas assessed future applications as having less risk (adjusted odds ratio [aOR] 0.51, 95% CI 0.35-0.76) than did dermatologists from rural areas. The situation was similar with the age groups, as, for example, dermatologists aged <50 years also expected lower risks (aOR 0.51, 95% CI 0.34-0.77) than those aged ≥50 years. There were no differences between sexes in use, but there were differences in knowledge and expectation; for example, male participants assessed their confidence in using digital applications as higher (aOR 1.44, 95% CI 1.01-2.04) than did female participants. CONCLUSIONS During the pandemic, the use of digital applications in dermatology increased but still remained at a moderate level. The regional and age-related disparities identified indicate the need for further action to ensure equal access to digital care.
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Affiliation(s)
- Matthias Augustin
- Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Patrick Reinders
- Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Toni Maria Janke
- Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | | | | | - Natalia Kirsten
- Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Alexander Zink
- Department of Dermatology and Allergy, School of Medicine Technical University of Munich, Munich, Germany
| | - Marina Otten
- Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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Maurya A, Stanley RJ, Lama N, Nambisan AK, Patel G, Saeed D, Swinfard S, Smith C, Jagannathan S, Hagerty JR, Stoecker WV. Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:92-106. [PMID: 38343238 DOI: 10.1007/s10278-023-00924-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 03/02/2024]
Abstract
A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.
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Affiliation(s)
- Akanksha Maurya
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | - R Joe Stanley
- Missouri University of Science &Technology, Rolla, MO, 65209, USA.
| | - Norsang Lama
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | - Anand K Nambisan
- Missouri University of Science &Technology, Rolla, MO, 65209, USA
| | | | | | | | - Colin Smith
- A.T. Still, University of Health Sciences, Kirksville, MO, USA
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Crawford ME, Kamali K, Dorey RA, MacIntyre OC, Cleminson K, MacGillivary ML, Green PJ, Langley RG, Purdy KS, DeCoste RC, Gruchy JR, Pasternak S, Oakley A, Hull PR. Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients. J Cutan Med Surg 2024; 28:37-43. [PMID: 38156628 PMCID: PMC10908200 DOI: 10.1177/12034754231216967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
INTRODUCTION Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. METHODS Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. RESULTS Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI's ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. CONCLUSION AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.
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Affiliation(s)
- Madeleine E. Crawford
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Kiyana Kamali
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Rachel A. Dorey
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Olivia C. MacIntyre
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Kristyna Cleminson
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Michael L. MacGillivary
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Peter J. Green
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Richard G. Langley
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Kerri S. Purdy
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Ryan C. DeCoste
- Department of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada
| | - Jennette R. Gruchy
- Department of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada
| | - Sylvia Pasternak
- Department of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada
| | - Amanda Oakley
- Department of Medicine, Waikato Clinical Campus, University of Auckland, Hamilton, New Zealand
| | - Peter R. Hull
- Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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21
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Zhang Y, Hu Y, Li K, Pan X, Mo X, Zhang H. Exploring the influence of transformer-based multimodal modeling on clinicians' diagnosis of skin diseases: A quantitative analysis. Digit Health 2024; 10:20552076241257087. [PMID: 38784049 PMCID: PMC11113036 DOI: 10.1177/20552076241257087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Objectives The study aimed to propose a multimodal model that incorporates both macroscopic and microscopic images and analyze its influence on clinicians' decision-making with different levels of experience. Methods First, we constructed a multimodal dataset for five skin disorders. Next, we trained unimodal models on three different types of images and selected the best-performing models as the base learners. Then, we used a soft voting strategy to create the multimodal model. Finally, 12 clinicians were divided into three groups, with each group including one director dermatologist, one dermatologist-in-charge, one resident dermatologist, and one general practitioner. They were asked to diagnose the skin disorders in four unaided situations (macroscopic images only, dermatopathological images only, macroscopic and dermatopathological images, all images and metadata), and three aided situations (macroscopic images with model 1 aid, dermatopathological images with model 2&3 aid, all images with multimodal model 4 aid). The clinicians' diagnosis accuracy and time for each diagnosis were recorded. Results Among the trained models, the vision transformer (ViT) achieved the best performance, with accuracies of 0.8636, 0.9545, 0.9673, and AUCs of 0.9823, 0.9952, 0.9989 on the training set, respectively. However, on the external validation set, they only achieved accuracies of 0.70, 0.90, and 0.94, respectively. The multimodal model performed well compared to the unimodal models, achieving an accuracy of 0.98 on the external validation set. The results of logit regression analysis indicate that all models are helpful to clinicians in making diagnostic decisions [Odds Ratios (OR) > 1], while metadata does not provide assistance to clinicians (OR < 1). Linear analysis results indicate that metadata significantly increases clinicians' diagnosis time (P < 0.05), while model assistance does not (P > 0.05). Conclusions The results of this study suggest that the multimodal model effectively improves clinicians' diagnostic performance without significantly increasing the diagnostic time. However, further large-scale prospective studies are necessary.
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Affiliation(s)
- Yujiao Zhang
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Yunfeng Hu
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Ke Li
- School of the First Clinical Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiangjun Pan
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xiaoling Mo
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Hong Zhang
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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Kushimo OO, Salau AO, Adeleke OJ, Olaoye DS. Deep learning model to improve melanoma detection in people of color. ARAB JOURNAL OF BASIC AND APPLIED SCIENCES 2023. [DOI: 10.1080/25765299.2023.2170066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Affiliation(s)
- Oluwatobi O. Kushimo
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Ayodeji Olalekan Salau
- Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
| | - Oladapo J. Adeleke
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Doyinsola S. Olaoye
- Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
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23
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Delyon J, Schmauch B, Jacob Y, Battistella M, Lebbé C. Letter to the Editor re: Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists. Eur J Cancer 2023; 195:113394. [PMID: 37891064 DOI: 10.1016/j.ejca.2023.113394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023]
Affiliation(s)
- Julie Delyon
- Department of Dermato-Oncology, AP-HP Saint-Louis Hospital, DMU ICARE, Université Paris Cité, INSERM U976 HIPI Team 1, F-75010 Paris, France.
| | | | - Yannick Jacob
- Innovation and Data Unit, IT Department, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Maxime Battistella
- Department of Pathology, AP-HP Saint-Louis Hospital, Université Paris Cité, INSERM U976 HIPI Team 1, F-75010 Paris, France
| | - Céleste Lebbé
- Department of Dermato-Oncology, AP-HP Saint-Louis Hospital, DMU ICARE, Université Paris Cité, INSERM U976 HIPI Team 1, F-75010 Paris, France
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Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol 2023; 96:82-99. [PMID: 37783319 DOI: 10.1016/j.semcancer.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 08/27/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.
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Affiliation(s)
- Yuting Jiang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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Li J, Du D, Zhang J, Liu W, Wang J, Wei X, Xue L, Li X, Diao P, Zhang L, Jiang X. Development and validation of an artificial intelligence-powered acne grading system incorporating lesion identification. Front Med (Lausanne) 2023; 10:1255704. [PMID: 37869155 PMCID: PMC10587552 DOI: 10.3389/fmed.2023.1255704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 09/12/2023] [Indexed: 10/24/2023] Open
Abstract
Background The management of acne requires the consideration of its severity; however, a universally adopted evaluation system for clinical practice is lacking. Artificial intelligence (AI) evaluation systems hold the promise of enhancing the efficiency and reproducibility of assessments. Artificial intelligence (AI) evaluation systems offer the potential to enhance the efficiency and reproducibility of assessments in this domain. While the identification of skin lesions represents a crucial component of acne evaluation, existing AI systems often overlook lesion identification or fail to integrate it with severity assessment. This study aimed to develop an AI-powered acne grading system and compare its performance with physician image-based scoring. Methods A total of 1,501 acne patients were included in the study, and standardized pictures were obtained using the VISIA system. The initial evaluation involved 40 stratified sampled frontal photos assessed by seven dermatologists. Subsequently, the three doctors with the highest inter-rater agreement annotated the remaining 1,461 images, which served as the dataset for the development of the AI system. The dataset was randomly divided into two groups: 276 images were allocated for training the acne lesion identification platform, and 1,185 images were used to assess the severity of acne. Results The average precision of our model for skin lesion identification was 0.507 and the average recall was 0.775. The AI severity grading system achieved good agreement with the true label (linear weighted kappa = 0.652). After integrating the lesion identification results into the severity assessment with fixed weights and learnable weights, the kappa rose to 0.737 and 0.696, respectively, and the entire evaluation on a Linux workstation with a Tesla K40m GPU took less than 0.1s per picture. Conclusion This study developed a system that detects various types of acne lesions and correlates them well with acne severity grading, and the good accuracy and efficiency make this approach potentially an effective clinical decision support tool.
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Affiliation(s)
- Jiaqi Li
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Dan Du
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jianwei Zhang
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Wenjie Liu
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Junyou Wang
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Xin Wei
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Li Xue
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
| | - Xiaoxue Li
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
| | - Ping Diao
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
| | - Lei Zhang
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Xian Jiang
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Dermatology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Clinical Institute of Inflammation and Immunology, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
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Van Molle P, Mylle S, Verbelen T, De Boom C, Vankeirsbilck B, Verhaeghe E, Dhoedt B, Brochez L. Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis: Complementary information that may affect decision-making. Exp Dermatol 2023; 32:1744-1751. [PMID: 37534916 DOI: 10.1111/exd.14892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 07/04/2023] [Accepted: 07/13/2023] [Indexed: 08/04/2023]
Abstract
In dermatology, deep learning may be applied for skin lesion classification. However, for a given input image, a neural network only outputs a label, obtained using the class probabilities, which do not model uncertainty. Our group developed a novel method to quantify uncertainty in stochastic neural networks. In this study, we aimed to train such network for skin lesion classification and evaluate its diagnostic performance and uncertainty, and compare the results to the assessments by a group of dermatologists. By passing duplicates of an image through such a stochastic neural network, we obtained distributions per class, rather than a single probability value. We interpreted the overlap between these distributions as the output uncertainty, where a high overlap indicated a high uncertainty, and vice versa. We had 29 dermatologists diagnose a series of skin lesions and rate their confidence. We compared these results to those of the network. The network achieved a sensitivity and specificity of 50% and 88%, comparable to the average dermatologist (respectively 68% and 73%). Higher confidence/less uncertainty was associated with better diagnostic performance both in the neural network and in dermatologists. We found no correlation between the uncertainty of the neural network and the confidence of dermatologists (R = -0.06, p = 0.77). Dermatologists should not blindly trust the output of a neural network, especially when its uncertainty is high. The addition of an uncertainty score may stimulate the human-computer interaction.
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Affiliation(s)
- Pieter Van Molle
- IDLab, Department of Information Technology, Ghent University-IMEC, Ghent, Belgium
| | - Sofie Mylle
- Department of Dermatology, Ghent University Hospital, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Tim Verbelen
- IDLab, Department of Information Technology, Ghent University-IMEC, Ghent, Belgium
| | - Cedric De Boom
- IDLab, Department of Information Technology, Ghent University-IMEC, Ghent, Belgium
| | - Bert Vankeirsbilck
- IDLab, Department of Information Technology, Ghent University-IMEC, Ghent, Belgium
| | - Evelien Verhaeghe
- Department of Dermatology, Ghent University Hospital, Ghent, Belgium
| | - Bart Dhoedt
- IDLab, Department of Information Technology, Ghent University-IMEC, Ghent, Belgium
| | - Lieve Brochez
- Department of Dermatology, Ghent University Hospital, Ghent, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent, Belgium
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Menzies SW, Sinz C, Menzies M, Lo SN, Yolland W, Lingohr J, Razmara M, Tschandl P, Guitera P, Scolyer RA, Boltz F, Borik-Heil L, Herbert Chan H, Chromy D, Coker DJ, Collgros H, Eghtedari M, Corral Forteza M, Forward E, Gallo B, Geisler S, Gibson M, Hampel A, Ho G, Junez L, Kienzl P, Martin A, Moloney FJ, Regio Pereira A, Ressler JM, Richter S, Silic K, Silly T, Skoll M, Tittes J, Weber P, Weninger W, Weiss D, Woo-Sampson P, Zilberg C, Kittler H. Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial. Lancet Digit Health 2023; 5:e679-e691. [PMID: 37775188 DOI: 10.1016/s2589-7500(23)00130-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 06/27/2023] [Accepted: 07/06/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Diagnosis of skin cancer requires medical expertise, which is scarce. Mobile phone-powered artificial intelligence (AI) could aid diagnosis, but it is unclear how this technology performs in a clinical scenario. Our primary aim was to test in the clinic whether there was equivalence between AI algorithms and clinicians for the diagnosis and management of pigmented skin lesions. METHODS In this multicentre, prospective, diagnostic, clinical trial, we included specialist and novice clinicians and patients from two tertiary referral centres in Australia and Austria. Specialists had a specialist medical qualification related to diagnosing and managing pigmented skin lesions, whereas novices were dermatology junior doctors or registrars in trainee positions who had experience in examining and managing these lesions. Eligible patients were aged 18-99 years and had a modified Fitzpatrick I-III skin type; those in the diagnostic trial were undergoing routine excision or biopsy of one or more suspicious pigmented skin lesions bigger than 3 mm in the longest diameter, and those in the management trial had baseline total-body photographs taken within 1-4 years. We used two mobile phone-powered AI instruments incorporating a simple optical attachment: a new 7-class AI algorithm and the International Skin Imaging Collaboration (ISIC) AI algorithm, which was previously tested in a large online reader study. The reference standard for excised lesions in the diagnostic trial was histopathological examination; in the management trial, the reference standard was a descending hierarchy based on histopathological examination, comparison of baseline total-body photographs, digital monitoring, and telediagnosis. The main outcome of this study was to compare the accuracy of expert and novice diagnostic and management decisions with the two AI instruments. Possible decisions in the management trial were dismissal, biopsy, or 3-month monitoring. Decisions to monitor were considered equivalent to dismissal (scenario A) or biopsy of malignant lesions (scenario B). The trial was registered at the Australian New Zealand Clinical Trials Registry ACTRN12620000695909 (Universal trial number U1111-1251-8995). FINDINGS The diagnostic study included 172 suspicious pigmented lesions (84 malignant) from 124 patients and the management study included 5696 pigmented lesions (18 malignant) from the whole body of 66 high-risk patients. The diagnoses of the 7-class AI algorithm were equivalent to the specialists' diagnoses (absolute accuracy difference 1·2% [95% CI -6·9 to 9·2]) and significantly superior to the novices' ones (21·5% [13·1 to 30·0]). The diagnoses of the ISIC AI algorithm were significantly inferior to the specialists' diagnoses (-11·6% [-20·3 to -3·0]) but significantly superior to the novices' ones (8·7% [-0·5 to 18·0]). The best 7-class management AI was significantly inferior to specialists' management (absolute accuracy difference in correct management decision -0·5% [95% CI -0·7 to -0·2] in scenario A and -0·4% [-0·8 to -0·05] in scenario B). Compared with the novices' management, the 7-class management AI was significantly inferior (-0·4% [-0·6 to -0·2]) in scenario A but significantly superior (0·4% [0·0 to 0·9]) in scenario B. INTERPRETATION The mobile phone-powered AI technology is simple, practical, and accurate for the diagnosis of suspicious pigmented skin cancer in patients presenting to a specialist setting, although its usage for management decisions requires more careful execution. An AI algorithm that was superior in experimental studies was significantly inferior to specialists in a real-world scenario, suggesting that caution is needed when extrapolating results of experimental studies to clinical practice. FUNDING MetaOptima Technology.
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Affiliation(s)
- Scott W Menzies
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia.
| | - Christoph Sinz
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Michelle Menzies
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia; Skintography, Sydney, NSW, Australia
| | - Serigne N Lo
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
| | | | | | | | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Pascale Guitera
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Richard A Scolyer
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW, Australia
| | - Florentina Boltz
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Liliane Borik-Heil
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Hsien Herbert Chan
- Department of Dermatology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - David Chromy
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - David J Coker
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Department of Melanoma and Surgical Oncology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Helena Collgros
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Maryam Eghtedari
- Department of Dermatology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Marina Corral Forteza
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Emily Forward
- Department of Dermatology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Bruna Gallo
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Stephanie Geisler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Matthew Gibson
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Department of Dermatology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Amelie Hampel
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Genevieve Ho
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Department of Dermatology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Laura Junez
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Philipp Kienzl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Arthur Martin
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
| | - Fergal J Moloney
- Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Amanda Regio Pereira
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia; Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | | | - Susanne Richter
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Katharina Silic
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Thomas Silly
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Michael Skoll
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Julia Tittes
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Philipp Weber
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Weninger
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Doris Weiss
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Ping Woo-Sampson
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Catherine Zilberg
- Department of Dermatology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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Miller IJ, Stapelberg M, Rosic N, Hudson J, Coxon P, Furness J, Walsh J, Climstein M. Implementation of artificial intelligence for the detection of cutaneous melanoma within a primary care setting: prevalence and types of skin cancer in outdoor enthusiasts. PeerJ 2023; 11:e15737. [PMID: 37576493 PMCID: PMC10416769 DOI: 10.7717/peerj.15737] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/20/2023] [Indexed: 08/15/2023] Open
Abstract
Background There is enthusiasm for implementing artificial intelligence (AI) to assist clinicians detect skin cancer. Performance metrics of AI from dermoscopic images have been promising, with studies documenting sensitivity and specificity values equal to or superior to specialists for the detection of malignant melanomas (MM). Early detection rates would particularly benefit Australia, which has the worlds highest incidence of MM per capita. The detection of skin cancer may be delayed due to late screening or the inherent difficulty in diagnosing early skin cancers which often have a paucity of clinical features and may blend into sun damaged skin. Individuals who participate in outdoor sports and recreation experience high levels of intermittent ultraviolet radiation (UVR), which is associated with the development of skin cancer, including MM. This research aimed to assess the prevalence of skin cancer in individuals who regularly participate in activities outdoors and to report the performance parameters of a commercially available AI-powered software to assess the predictive risk of MM development. Methods Cross-sectional study design incorporating a survey, total body skin cancer screening and AI-embedded software capable of predictive scoring of queried MM. Results A total of 423 participants consisting of surfers (n = 108), swimmers (n = 60) and walkers/runners (n = 255) participated. Point prevalence for MM was highest for surfers (6.48%), followed by walkers/runners (4.3%) and swimmers (3.33%) respectively. When compared to the general Australian population, surfers had the highest odds ratio (OR) for MM (OR 119.8), followed by walkers/runners (OR 79.74), and swimmers (OR 61.61) rounded out the populations. Surfers and swimmers reported comparatively lower lifetime hours of sun exposure (5,594 and 5,686, respectively) but more significant amounts of activity within peak ultraviolet index compared with walkers/runners (9,554 h). A total of 48 suspicious pigmented lesions made up of histopathology-confirmed MM (n = 15) and benign lesions (n = 33) were identified. The performance of the AI from this clinical population was found to have a sensitivity of 53.33%, specificity of 54.44% and accuracy of 54.17%. Conclusions Rates of both keratinocyte carcinomas and MM were notably higher in aquatic and land-based enthusiasts compared to the general Australian population. These findings further highlight the clinical importance of sun-safe protection measures and regular skin screening in individuals who spend significant time outdoors. The use of AI in the early identification of MM is promising. However, the lower-than-expected performance metrics of the AI software used in this study indicated reservations should be held before recommending this particular version of this AI software as a reliable adjunct for clinicians in skin imaging diagnostics in patients with potentially sun damaged skin.
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Affiliation(s)
- Ian J. Miller
- Aquatic Based Research, Southern Cross University, Bilinga, Queensland, Australia
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
| | - Michael Stapelberg
- Aquatic Based Research, Southern Cross University, Bilinga, Queensland, Australia
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
- Specialist Suite, John Flynn Hospital, Tugun, Queensland, Australia
| | - Nedeljka Rosic
- Aquatic Based Research, Southern Cross University, Bilinga, Queensland, Australia
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
| | - Jeremy Hudson
- Aquatic Based Research, Southern Cross University, Bilinga, Queensland, Australia
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
- North Queensland Skin Centre, Townsville, Queensland, Australia
| | - Paul Coxon
- North Queensland Skin Centre, Townsville, Queensland, Australia
| | - James Furness
- Water Based Research Unit, Bond University, Robina, Queensland, Australia
| | - Joe Walsh
- Sport Science Institute, Sydney, NSW, Australia
- AI Consulting Group, Sydney, NSW, Australia
| | - Mike Climstein
- Aquatic Based Research, Southern Cross University, Bilinga, Queensland, Australia
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
- Water Based Research Unit, Bond University, Robina, Queensland, Australia
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, NSW, Australia
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Schuh S, Schiele S, Thamm J, Kranz S, Welzel J, Blum A. Implementation of a dermatoscopy curriculum during residency at Augsburg University Hospital in Germany. J Dtsch Dermatol Ges 2023; 21:872-879. [PMID: 37235503 DOI: 10.1111/ddg.15115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/04/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND AND OBJECTIVES To date, there is no structured program for dermatoscopy training during residency in Germany. Whether and how much dermatoscopy training is acquired is left to the initiative of each resident, although dermatoscopy is one of the core competencies of dermatological training and daily practice. The aim of the study was to establish a structured dermatoscopy curriculum during residency at the University Hospital Augsburg. PATIENTS AND METHODS An online platform with dermatoscopy modules was created, accessible regardless of time and place. Practical skills were acquired under the personal guidance of a dermatoscopy expert. Participants were tested on their level of knowledge before and after completing the modules. Test scores on management decisions and correct dermatoscopic diagnosis were analyzed. RESULTS Results of 28 participants showed improvements in management decisions from pre- to posttest (74.0% vs. 89.4%) and in dermatoscopic accuracy (65.0% vs. 85.6%). Pre- vs. posttest differences in test score (7.05/10 vs. 8.94/10 points) and correct diagnosis were significant (p < 0.001). CONCLUSIONS The dermatoscopy curriculum increases the number of correct management decisions and dermatoscopy diagnoses. This will result in more skin cancers being detected, and fewer benign lesions being excised. The curriculum can be offered to other dermatology training centers and medical professionals.
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Affiliation(s)
- Sandra Schuh
- Department of Dermatology and Allergology, University Hospital Augsburg, Augsburg, Germany
| | - Stefan Schiele
- Institute of Mathematics, University of Augsburg, Augsburg, Germany
| | - Janis Thamm
- Department of Dermatology and Allergology, University Hospital Augsburg, Augsburg, Germany
| | - Stefanie Kranz
- Department of Dermatology and Allergology, University Hospital Augsburg, Augsburg, Germany
| | - Julia Welzel
- Department of Dermatology and Allergology, University Hospital Augsburg, Augsburg, Germany
| | - Andreas Blum
- Public, Private and Teaching Practice of Dermatology, Konstanz, Germany
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Schuh S, Schiele S, Thamm J, Kranz S, Welzel J, Blum A. Implementierung eines Dermatoskopie-Curriculums in der Facharztausbildung am Universitätsklinikum Augsburg. J Dtsch Dermatol Ges 2023; 21:872-881. [PMID: 37574685 DOI: 10.1111/ddg.15115_g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/04/2023] [Indexed: 08/15/2023]
Abstract
ZusammenfassungHintergrund und ZieleBislang gibt es in Deutschland kein strukturiertes Programm für die Dermatoskopieausbildung während der Facharztausbildung. Es bleibt der Initiative des einzelnen Assistenzarztes überlassen, ob und in welchem Umfang er sich in der Dermatoskopie weiterbildet, obwohl die Dermatoskopie zu den Kernkompetenzen der dermatologischen Ausbildung und der täglichen Praxis gehört. Ziel der Studie war die Etablierung eines strukturierten Dermatoskopie‐Curriculums während der dermatologischen Facharztausbildung am Universitätsklinikum Augsburg.Patienten und MethodikEs wurde eine Online‐Plattform mit Dermatoskopie‐Modulen geschaffen, auf die von überall und jederzeit zugegriffen werden kann. Praktische Fertigkeiten wurden unter individueller Anleitung eines Dermatoskopie‐Experten erworben. Die Teilnehmer wurden vor und nach Abschluss der Module auf ihren Wissensstand getestet. Die Testergebnisse zum therapeutischen Management und zur korrekten dermatoskopischen Diagnose wurden analysiert.ErgebnisseDie Ergebnisse der 28 Teilnehmer verbesserten sich vom Eingangs‐ zum Abschlusstest bei der Managemententscheidung (74,0% vs. 89,4%) und bei der dermatoskopischen Genauigkeit (65,0% vs. 85,6%). Die Unterschiede zwischen Eingangs‐ und Abschlusstest bei der Gesamtpunktzahl (7,05/10 vs. 8,94/10 Punkte) und bei der richtigen Diagnose waren signifikant (p < 0,001).SchlussfolgerungenDas Dermatoskopie‐Curriculum verbessert die Managemententscheidungen und die dermatoskopische Diagnostik der Teilnehmer. Das wird dazu führen, dass mehr Hautkrebsfälle erkannt werden und weniger gutartige Läsionen reseziert werden müssen. Das Curriculum kann anderen dermatologischen Ausbildungszentren und Gesundheitsberufen angeboten werden.
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Affiliation(s)
- Sandra Schuh
- Klinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg
| | | | - Janis Thamm
- Klinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg
| | - Stefanie Kranz
- Klinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg
| | - Julia Welzel
- Klinik für Dermatologie und Allergologie, Universitätsklinikum Augsburg
| | - Andreas Blum
- Hautarzt- und Lehrpraxis für Dermatologie, Konstanz
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Malvehy J, Dreno B, Barba E, Dirshka T, Fumero E, Greis C, Gupta G, Lacarrubba F, Micali G, Moreno D, Pellacani G, Sampietro-Colom L, Stratigos A, Puig S. Smart e-Skin Cancer Care in Europe During and after the Covid-19 Pandemic: a Multidisciplinary Expert Consensus. Dermatol Pract Concept 2023; 13:e2023181. [PMID: 37557116 PMCID: PMC10412091 DOI: 10.5826/dpc.1303a181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 08/11/2023] Open
Abstract
INTRODUCTION Melanoma is the deadliest of all the skin cancers and its incidence is increasing every year in Europe. Patients with melanoma often present late to the specialist and treatment is delayed for many reasons (delay in patient consultation, misdiagnosis by general practitioners, and/or limited access to dermatologists). Beyond this, there are significant inequalities in skin cancer between population groups within the same country and between countries across Europe. The emergence of the COVID-19 pandemic only aggravated these health deficiencies. OBJECTIVES The aim was to create an expert opinion about the challenges in skin cancer management in Europe during the post COVID-19 acute pandemic and to identify and discuss the implementation of new technologies (including e-health and artificial intelligence defined as "Smart Skin Cancer Care") to overcome them. METHODS For this purpose, an ad-hoc questionnaire with items addressing topics of skin cancer care was developed, answered independently and discussed by a multidisciplinary European panel of experts comprising dermatologists, dermato-oncologists, patient advocacy representatives, digital health technology experts, and health technology assessment experts. RESULTS After all panel of experts discussions, a multidisciplinary expert opinion was created. CONCLUSIONS As a conclusion, the access to dermatologists is difficult and will be aggravated in the near future. This fact, together with important differences in Skin Cancer Care in Europe, suggest the need of a new approach to skin health, prevention and disease management paradigm (focused on integration of new technologies) to minimize the impact of skin cancer and to ensure optimal quality and equity.
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Affiliation(s)
- Josep Malvehy
- Dermatology Department. Hospital Clinic of Barcelona, Spain
- University of Barcelona, Barcelona, Spain. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Biomedical Research Networking Centre on Rare Diseases (CIBERER), ISCIII, Barcelona, Spain
| | - Brigitte Dreno
- Department of Dermatolo-Cancerology, CHU Nantes, CIC 1413, CRCINA, University Nantes, Nantes, France
| | - Enric Barba
- Spanish Melanoma Association, Barcelona, Spain
| | - Thomas Dirshka
- Centroderm Clinic, Wuppertal, and Faculty of Health, University Witten-Herdecke, Witten, Germany
| | | | - Christian Greis
- Department Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | - Girish Gupta
- University Department of Dermatology, Edinburgh Royal Infirmary, Lauriston Building, Edinburgh, UK
| | | | | | - David Moreno
- Dermatology Department, University Hospital Virgen Macarena, Seville, Spain
| | - Giovanni Pellacani
- Dermatology Department. Università degli Studi di Roma La Sapienza. Roma, Italy
| | - Laura Sampietro-Colom
- Assessment of Innovations and New Technologies Unit, Hospital Clínic of Barcelona, University of Barcelona, Barcelona, Spain
| | - Alexander Stratigos
- Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodestrian University of Athens, Athens, Greece
| | - Susanna Puig
- Dermatology Department. Hospital Clinic of Barcelona, Spain
- University of Barcelona, Barcelona, Spain. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. Biomedical Research Networking Centre on Rare Diseases (CIBERER), ISCIII, Barcelona, Spain
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Cerminara SE, Cheng P, Kostner L, Huber S, Kunz M, Maul JT, Böhm JS, Dettwiler CF, Geser A, Jakopović C, Stoffel LM, Peter JK, Levesque M, Navarini AA, Maul LV. Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening? Eur J Cancer 2023; 190:112954. [PMID: 37453242 DOI: 10.1016/j.ejca.2023.112954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/13/2023] [Accepted: 06/17/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Convolutional neural networks (CNNs) have outperformed dermatologists in classifying pigmented skin lesions under artificial conditions. We investigated, for the first time, the performance of three-dimensional (3D) and two-dimensional (2D) CNNs and dermatologists in the early detection of melanoma in a real-world setting. METHODS In this prospective study, 1690 melanocytic lesions in 143 patients with high-risk criteria for melanoma were evaluated by dermatologists, 2D-FotoFinder-ATBM and 3D-Vectra WB360 total body photography (TBP). Excision was based on the dermatologists' dichotomous decision, an elevated CNN risk score (study-specific malignancy cut-off: FotoFinder >0.5, Vectra >5.0) and/or the second dermatologist's assessment with CNN support. The diagnostic accuracy of the 2D and 3D CNN classification was compared with that of the dermatologists and the augmented intelligence based on histopathology and dermatologists' assessment. Secondary end-points included reproducibility of risk scores and naevus counts per patient by medical staff (gold standard) compared to automated 3D and 2D TBP CNN counts. RESULTS The sensitivity, specificity, and receiver operating characteristics area under the curve (ROC-AUC) for risk-score-assessments compared to histopathology of 3D-CNN with 95% confidence intervals (CI) were 90.0%, 64.6% and 0.92 (CI 0.85-1.00), respectively. While dermatologists and augmented intelligence achieved the same sensitivity (90%) and comparable classification ROC-AUC (0.91 [CI 0.80-1.00], 0.88 [CI 0.77-1.00]) with 3D-CNN, their specificity was superior (92.3% and 86.2%, respectively). The 2D-CNN (sensitivity: 70%, specificity: 40%, ROC-AUC: 0.68 [CI 0.46-0.90]) was outperformed by 3D CNN and dermatologists. The 3D-CNN showed a higher correlation coefficient for repeated measurements of 246 lesions (R = 0.89) than the 2D-CNN (R = 0.79). The mean naevus count per patient varied significantly (gold standard: 210 lesions; 3D-CNN: 469; 2D-CNN: 1324; p < 0.0001). CONCLUSIONS Our study emphasises the importance of validating the classification of CNNs in real life. The novel 3D-CNN device outperformed the 2D-CNN and achieved comparable sensitivity with dermatologists. The low specificity of CNNs and the lack of automated counting of TBP nevi currently limit the use of augmented intelligence in clinical practice.
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Affiliation(s)
- Sara E Cerminara
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Phil Cheng
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | - Lisa Kostner
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Stephanie Huber
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Michael Kunz
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Julia-Tatjana Maul
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland; Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Jette S Böhm
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Chiara F Dettwiler
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Anna Geser
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Cécile Jakopović
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Livia M Stoffel
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Jelissa K Peter
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Mitchell Levesque
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | | | - Lara Valeska Maul
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland.
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Haugsten ER, Vestergaard T, Trettin B. Experiences Regarding Use and Implementation of Artificial Intelligence-Supported Follow-Up of Atypical Moles at a Dermatological Outpatient Clinic: Qualitative Study. JMIR DERMATOLOGY 2023; 6:e44913. [PMID: 37632937 PMCID: PMC10335120 DOI: 10.2196/44913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly used in numerous medical fields. In dermatology, AI can be used in the form of computer-assisted diagnosis (CAD) systems when assessing and diagnosing skin lesions suspicious of melanoma, a potentially lethal skin cancer with rising incidence all over the world. In particular, CAD may be a valuable tool in the follow-up of patients with high risk of developing melanoma, such as patients with multiple atypical moles. One such CAD system, ATBM Master (FotoFinder), can execute total body dermoscopy (TBD). This process comprises automatically photographing a patient´s entire body and then neatly displaying moles on a computer screen, grouped according to their clinical relevance. Proprietary FotoFinder algorithms underlie this organized presentation of moles. In addition, ATBM Master's optional convoluted neural network (CNN)-based Moleanalyzer Pro software can be used to further assess moles and estimate their probability of malignancy. OBJECTIVE Few qualitative studies have been conducted on the implementation of AI-supported procedures in dermatology. Therefore, the purpose of this study was to investigate how health care providers experience the use and implementation of a CAD system like ATBM Master, in particular its TBD module. In this way, the study aimed to elucidate potential barriers to the application of such new technology. METHODS We conducted a thematic analysis based on 2 focus group interviews with 14 doctors and nurses regularly working in an outpatient pigmented lesions clinic. RESULTS Surprisingly, the study revealed that only 3 participants had actual experience using the TBD module. Even so, all participants were able to provide many notions and anticipations about its use, resulting in 3 major themes emerging from the interviews. First, several organizational matters were revealed to be a barrier to consistent use of the ATBM Master's TBD module, namely lack of guidance, time pressure, and insufficient training. Second, the study found that the perceived benefits of TBD were the ability to objectively detect and monitor subtle lesion changes and unbiasedness of the procedure. Imprecise identification of moles, inability to photograph certain areas, and substandard technical aspects were the perceived weaknesses. Lastly, the study found that clinicians were open to use AI-powered technology and that the TBD module was considered a supplementary tool to aid the medical staff, rather than a replacement of the clinician. CONCLUSIONS Demonstrated by how few of the participants had actual experience with the TBD module, this study showed that implementation of new technology does not occur automatically. It highlights the importance of having a strategy for implementation to ensure the optimized application of CAD tools. The study identified areas that could be improved when implementing AI-powered technology, as well as providing insight on how medical staff anticipated and experienced the use of a CAD device in dermatology.
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Affiliation(s)
- Elisabeth Rygvold Haugsten
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Tine Vestergaard
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Bettina Trettin
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
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Liutkus J, Kriukas A, Stragyte D, Mazeika E, Raudonis V, Galetzka W, Stang A, Valiukeviciene S. Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses. Diagnostics (Basel) 2023; 13:2139. [PMID: 37443533 DOI: 10.3390/diagnostics13132139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based "You Only Look Once" neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm's decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm's sensitivity and specificity for melanomas were 0.88 (0.71-0.96) and 0.87 (0.76-0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77-0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60-0.90). The algorithm's sensitivity for seborrheic keratoses was 0.52 (0.34-0.69). The smartphone-based "You Only Look Once" neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm's sensitivity for seborrheic keratoses.
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Affiliation(s)
- Jokubas Liutkus
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Arturas Kriukas
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Dominyka Stragyte
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Erikas Mazeika
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Vidas Raudonis
- Artificial Intelligence Center, Kaunas University of Technology, 51423 Kaunas, Lithuania
| | - Wolfgang Galetzka
- Institute of Medical Informatics, Biometrics and Epidemiology, University Hospital Essen, 45130 Essen, Germany
| | - Andreas Stang
- Institute of Medical Informatics, Biometrics and Epidemiology, University Hospital Essen, 45130 Essen, Germany
| | - Skaidra Valiukeviciene
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
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Grossarth S, Mosley D, Madden C, Ike J, Smith I, Huo Y, Wheless L. Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods. Curr Oncol Rep 2023; 25:635-645. [PMID: 37000340 PMCID: PMC10339689 DOI: 10.1007/s11912-023-01407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/01/2023]
Abstract
PURPOSE OF REVIEW The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma. RECENT FINDINGS Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.
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Affiliation(s)
- Sarah Grossarth
- Quillen College of Medicine, East Tennessee State University, Johnson City, TN, USA
| | | | - Christopher Madden
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- State University of New York Downstate College of Medicine, Brooklyn, NY, USA
| | - Jacqueline Ike
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Meharry Medical College, Nashville, TN, USA
| | - Isabelle Smith
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Yuankai Huo
- Department of Computer Science and Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lee Wheless
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA.
- Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Tennessee Valley Healthcare System VA Medical Center, Nashville, TN, USA.
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Winkler JK, Blum A, Kommoss K, Enk A, Toberer F, Rosenberger A, Haenssle HA. Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine. JAMA Dermatol 2023; 159:621-627. [PMID: 37133847 PMCID: PMC10157508 DOI: 10.1001/jamadermatol.2023.0905] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/05/2023] [Indexed: 05/04/2023]
Abstract
Importance Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human with machine cooperation are lacking. Objective To assess whether dermatologists benefit from cooperation with a market-approved CNN in classifying melanocytic lesions. Design, Setting, and Participants In this prospective diagnostic 2-center study, dermatologists performed skin cancer screenings using naked-eye examination and dermoscopy. Dermatologists graded suspect melanocytic lesions by the probability of malignancy (range 0-1, threshold for malignancy ≥0.5) and indicated management decisions (no action, follow-up, excision). Next, dermoscopic images of suspect lesions were assessed by a market-approved CNN, Moleanalyzer Pro (FotoFinder Systems). The CNN malignancy scores (range 0-1, threshold for malignancy ≥0.5) were transferred to dermatologists with the request to re-evaluate lesions and revise initial decisions in consideration of CNN results. Reference diagnoses were based on histopathologic examination in 125 (54.8%) lesions or, in the case of nonexcised lesions, on clinical follow-up data and expert consensus. Data were collected from October 2020 to October 2021. Main Outcomes and Measures Primary outcome measures were diagnostic sensitivity and specificity of dermatologists alone and dermatologists cooperating with the CNN. Accuracy and receiver operator characteristic area under the curve (ROC AUC) were considered as additional measures. Results A total of 22 dermatologists detected 228 suspect melanocytic lesions (190 nevi, 38 melanomas) in 188 patients (mean [range] age, 53.4 [19-91] years; 97 [51.6%] male patients). Diagnostic sensitivity and specificity significantly improved when dermatologists additionally integrated CNN results into decision-making (mean sensitivity from 84.2% [95% CI, 69.6%-92.6%] to 100.0% [95% CI, 90.8%-100.0%]; P = .03; mean specificity from 72.1% [95% CI, 65.3%-78.0%] to 83.7% [95% CI, 77.8%-88.3%]; P < .001; mean accuracy from 74.1% [95% CI, 68.1%-79.4%] to 86.4% [95% CI, 81.3%-90.3%]; P < .001; and mean ROC AUC from 0.895 [95% CI, 0.836-0.954] to 0.968 [95% CI, 0.948-0.988]; P = .005). In addition, the CNN alone achieved a comparable sensitivity, higher specificity, and higher diagnostic accuracy compared with dermatologists alone in classifying melanocytic lesions. Moreover, unnecessary excisions of benign nevi were reduced by 19.2%, from 104 (54.7%) of 190 benign nevi to 84 nevi when dermatologists cooperated with the CNN (P < .001). Most lesions were examined by dermatologists with 2 to 5 years (96, 42.1%) or less than 2 years of experience (78, 34.2%); others (54, 23.7%) were evaluated by dermatologists with more than 5 years of experience. Dermatologists with less dermoscopy experience cooperating with the CNN had the most diagnostic improvement compared with more experienced dermatologists. Conclusions and Relevance In this prospective diagnostic study, these findings suggest that dermatologists may improve their performance when they cooperate with the market-approved CNN and that a broader application of this human with machine approach could be beneficial for dermatologists and patients.
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Affiliation(s)
- Julia K. Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Andreas Blum
- Public, Private and Teaching Practice of Dermatology, Konstanz, Germany
| | - Katharina Kommoss
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Alexander Enk
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Ferdinand Toberer
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Albert Rosenberger
- Institute of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
| | - Holger A. Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
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Kommoss KS, Winkler JK, Mueller-Christmann C, Bardehle F, Toberer F, Stolz W, Kraenke T, Hofmann-Wellenhof R, Blum A, Enk A, Rosenberger A, Haenssle HA. Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists. Eur J Cancer 2023; 185:53-60. [PMID: 36963352 DOI: 10.1016/j.ejca.2023.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically 'unclear' lesions may benefit from artificial intelligence support via convolutional neural networks (CNN). METHODS In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as 'benign', 'malignant', or 'unclear' and indicated their management decisions ('no action', 'follow-up', 'treatment/excision'). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images. RESULTS After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as 'unclear' and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 'follow-up' or 'no action') and 43.9% of 271 truly benign cases (119 'excision'). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained 'unclear' to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01). CONCLUSIONS Dermatologists mostly managed diagnostically 'unclear' FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions.
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Affiliation(s)
| | - Julia K Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | | | - Felicitas Bardehle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Ferdinand Toberer
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Wilhelm Stolz
- Department of Dermatology, Allergology and Environmental Medicine II, Hospital Thalkirchner Street, Munich, Germany
| | - Teresa Kraenke
- Department of Dermatology and Venerology, Medical University of Graz, Graz, Austria
| | | | - Andreas Blum
- Public, Private and Teaching Practice of Dermatology, Konstanz, Germany
| | - Alexander Enk
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Albert Rosenberger
- Department of Genetic Epidemiology, University of Goettingen, Goettingen, Germany
| | - Holger A Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
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Steele L, Tan XL, Olabi B, Gao JM, Tanaka RJ, Williams HC. Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review. J Eur Acad Dermatol Venereol 2023; 37:657-665. [PMID: 36514990 DOI: 10.1111/jdv.18814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 11/09/2022] [Indexed: 12/15/2022]
Abstract
Machine learning (ML) models for skin cancer recognition may have variable performance across different skin phototypes and skin cancer types. Overall performance metrics alone are insufficient to detect poor subgroup performance. We aimed (1) to assess whether studies of ML models reported results separately for different skin phototypes and rarer skin cancers, and (2) to graphically represent the skin cancer training datasets used by current ML models. In this systematic review, we searched PubMed, Embase and CENTRAL. We included all studies in medical journals assessing an ML technique for skin cancer diagnosis that used clinical or dermoscopic images from 1 January 2012 to 22 September 2021. No language restrictions were applied. We considered rarer skin cancers to be skin cancers other than pigmented melanoma, basal cell carcinoma and squamous cell carcinoma. We identified 114 studies for inclusion. Rarer skin cancers were included by 8/114 studies (7.0%), and results for a rarer skin cancer were reported separately in 1/114 studies (0.9%). Performance was reported across all skin phototypes in 1/114 studies (0.9%), but performance was uncertain in skin phototypes I and VI from minimal representation of the skin phototypes in the test dataset (9/3756 and 1/3756, respectively). For training datasets, although public datasets were most frequently used, with the most widely used being the International Skin Imaging Collaboration (ISIC) archive (65/114 studies, 57.0%), the largest datasets were private. Our review identified that most ML models did not report performance separately for rarer skin cancers and different skin phototypes. A degree of variability in ML model performance across subgroups is expected, but the current lack of transparency is not justifiable and risks models being used inappropriately in populations in whom accuracy is low.
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Affiliation(s)
- Lloyd Steele
- Department of Dermatology, The Royal London Hospital, London, UK.,Centre for Cell Biology and Cutaneous Research, Blizard Institute, Queen Mary University of London, London, UK
| | - Xiang Li Tan
- St George's University Hospitals NHS Foundation Trust, London, UK
| | - Bayanne Olabi
- Biosciences Institute, Newcastle University, Newcastle, UK
| | - Jing Mia Gao
- Department of Dermatology, The Royal London Hospital, London, UK
| | - Reiko J Tanaka
- Department of Bioengineering, Imperial College London, London, UK
| | - Hywel C Williams
- Centre of Evidence-Based Dermatology, School of Medicine, University of Nottingham, Nottingham, UK
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Wagner G, Raymond L, Paré G. Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis. JMIR MEDICAL EDUCATION 2023; 9:e45631. [PMID: 36947121 PMCID: PMC10131981 DOI: 10.2196/45631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place. OBJECTIVE Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice. METHODS We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t0 (February 2020, before the COVID-19 pandemic) and 138 responses at t1 (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data. RESULTS Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry). CONCLUSIONS Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.
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Affiliation(s)
- Gerit Wagner
- Faculty Information Systems and Applied Computer Sciences, University of Bamberg, Bamberg, Germany
| | - Louis Raymond
- Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
| | - Guy Paré
- Department of Information Technologies, École des Hautes Études commerciales Montréal, Montréal, QC, Canada
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Ukharov A, Shlivko I, Klemenova I, Garanina O, Uskova K, Mironycheva A, Stepanova Y. Skin cancer risk self-assessment using AI as a mass screening tool. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
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Maguire WF, Haley PH, Dietz CM, Hoffelder M, Brandt CS, Joyce R, Fitzgerald G, Minnier C, Sander C, Ferris LK, Paragh G, Arbesman J, Wang H, Mitchell KJ, Hughes EK, Kirkwood JM. Development and Narrow Validation of Computer Vision Approach to Facilitate Assessment of Change in Pigmented Cutaneous Lesions. JID INNOVATIONS 2023; 3:100181. [PMID: 36960318 PMCID: PMC10030255 DOI: 10.1016/j.xjidi.2023.100181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 11/10/2022] [Accepted: 11/16/2022] [Indexed: 01/10/2023] Open
Abstract
The documentation of the change in the number and appearance of pigmented cutaneous lesions over time is critical to the early detection of skin cancers and may provide preliminary signals of efficacy in early-phase therapeutic prevention trials for melanoma. Despite substantial progress in computer-aided diagnosis of melanoma, automated methods to assess the evolution of lesions are relatively undeveloped. This report describes the development and narrow validation of mathematical algorithms to register nevi between sequential digital photographs of large areas of skin and to align images for improved detection and quantification of changes. Serial posterior truncal photographs from a pre-existing database were processed and analyzed by the software, and the results were evaluated by a panel of clinicians using a separate Extensible Markup Language‒based application. The software had a high sensitivity for the detection of cutaneous lesions as small as 2 mm. The software registered lesions accurately, with occasional errors at the edges of the images. In one pilot study with 17 patients, the use of the software enabled clinicians to identify new and/or enlarged lesions in 3‒11 additional patients versus the unregistered images. Automated quantification of size change performed similarly to that of human raters. These results support the further development and broader validation of this technique.
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Affiliation(s)
- William F. Maguire
- Division of Hematology/Oncology, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
| | - Paul H. Haley
- Computer Vision Group, Veytel, Pittsburgh, Pennsylvania, USA
| | | | - Mike Hoffelder
- Computer Vision Group, Veytel, Pittsburgh, Pennsylvania, USA
| | - Clara S. Brandt
- Computer Vision Group, Veytel, Pittsburgh, Pennsylvania, USA
- Mount Holyoke College, South Hadley, Massachusetts, USA
| | - Robin Joyce
- Computer Vision Group, Veytel, Pittsburgh, Pennsylvania, USA
- Mount Holyoke College, South Hadley, Massachusetts, USA
| | - Georgia Fitzgerald
- Computer Vision Group, Veytel, Pittsburgh, Pennsylvania, USA
- Mount Holyoke College, South Hadley, Massachusetts, USA
| | | | - Cindy Sander
- UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
| | - Laura K. Ferris
- Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gyorgy Paragh
- Department of Dermatology, Roswell Park Comprehensive Cancer Institute, Buffalo, New York, USA
| | | | - Hong Wang
- School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Ellen K. Hughes
- Computer Vision Group, Veytel, Pittsburgh, Pennsylvania, USA
| | - John M. Kirkwood
- Division of Hematology/Oncology, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
- Correspondence: John M. Kirkwood, Division of Hematology/Oncology, Department of Medicine, School of Medicine, University of Pittsburgh, 5117 Centre Avenue, Suite 1.32, Pittsburgh, Pennsylvania 15213, USA.
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Kränke T, Tripolt-Droschl K, Röd L, Hofmann-Wellenhof R, Koppitz M, Tripolt M. New AI-algorithms on smartphones to detect skin cancer in a clinical setting-A validation study. PLoS One 2023; 18:e0280670. [PMID: 36791068 PMCID: PMC9931135 DOI: 10.1371/journal.pone.0280670] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 01/05/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The incidence of skin cancer is rising worldwide and there is medical need to optimize its early detection. This study was conducted to determine the diagnostic and risk-assessment accuracy of two new diagnosis-based neural networks (analyze and detect), which comply with the CE-criteria, in evaluating the malignant potential of various skin lesions on a smartphone. Of note, the intention of our study was to evaluate the performance of these medical products in a clinical setting for the first time. METHODS This was a prospective, single-center clinical study at one tertiary referral center in Graz, Austria. Patients, who were either scheduled for preventive skin examination or removal of at least one skin lesion were eligible for participation. Patients were assessed by at least two dermatologists and by the integrated algorithms on different mobile phones. The lesions to be recorded were randomly selected by the dermatologists. The diagnosis of the algorithm was stated as correct if it matched the diagnosis of the two dermatologists or the histology (if available). The histology was the reference standard, however, if both clinicians considered a lesion as being benign no histology was performed and the dermatologists were stated as reference standard. RESULTS A total of 238 patients with 1171 lesions (86 female; 36.13%) with an average age of 66.19 (SD = 17.05) was included. Sensitivity and specificity of the detect algorithm were 96.4% (CI 93.94-98.85) and 94.85% (CI 92.46-97.23); for the analyze algorithm a sensitivity of 95.35% (CI 93.45-97.25) and a specificity of 90.32% (CI 88.1-92.54) were achieved. DISCUSSION The studied neural networks succeeded analyzing the risk of skin lesions with a high diagnostic accuracy showing that they are sufficient tools in calculating the probability of a skin lesion being malignant. In conjunction with the wide spread use of smartphones this new AI approach opens the opportunity for a higher early detection rate of skin cancer with consecutive lower epidemiological burden of metastatic cancer and reducing health care costs. This neural network moreover facilitates the empowerment of patients, especially in regions with a low density of medical doctors. REGISTRATION Approved and registered at the ethics committee of the Medical University of Graz, Austria (Approval number: 30-199 ex 17/18).
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Affiliation(s)
- Teresa Kränke
- Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria
- * E-mail:
| | | | - Lukas Röd
- Medical University of Graz, Graz, Austria
| | | | | | - Michael Tripolt
- Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria
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Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5. J Clin Med 2023; 12:jcm12031134. [PMID: 36769781 PMCID: PMC9918106 DOI: 10.3390/jcm12031134] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.
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Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatol Ther (Heidelb) 2022; 12:2637-2651. [PMID: 36306100 PMCID: PMC9674813 DOI: 10.1007/s13555-022-00833-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/07/2022] [Indexed: 01/07/2023] Open
Abstract
Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.
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Affiliation(s)
- Konstantinos Liopyris
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
- Dermatology Department, Memorial Sloan Kettering Cancer Center, New York, NY, 10021, USA
| | - Stamatios Gregoriou
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece.
| | - Julia Dias
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
| | - Alexandros J Stratigos
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
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Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y. Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. J Clin Med 2022; 11:jcm11226826. [PMID: 36431301 PMCID: PMC9693628 DOI: 10.3390/jcm11226826] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. THE AIM OF THE STUDY For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.
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Affiliation(s)
- Zhouxiao Li
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | | | - Thilo Ludwig Schenck
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | - Riccardo Enzo Giunta
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | - Qingfeng Li
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Correspondence: (Q.L.); (Y.S.)
| | - Yangbai Sun
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Correspondence: (Q.L.); (Y.S.)
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Winkler JK, Haenssle HA. [Artificial intelligence-based classification for the diagnostics of skin cancer]. DERMATOLOGIE (HEIDELBERG, GERMANY) 2022; 73:838-844. [PMID: 36094608 DOI: 10.1007/s00105-022-05058-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Convolutional neural networks (CNN) achieve a level of performance comparable or even superior to dermatologists in the assessment of pigmented and nonpigmented skin lesions. In the analysis of images by artificial neural networks, images on a pixel level pass through various layers of the network with different graphic filters. Based on excellent study results, a first deep learning network (Moleanalyzer pro, Fotofinder Systems GmBH, Bad Birnbach, Germany) received market approval in Europe. However, such neural networks also reveal relevant limitations, whereby rare entities with insufficient training images are classified less adequately and image artifacts can lead to false diagnoses. Best results can ultimately be achieved in a cooperation of "man with machine". For future skin cancer screening, automated total body mapping is evaluated, which combines total body photography, automated data extraction and assessment of all relevant skin lesions.
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Affiliation(s)
- Julia K Winkler
- Universitätshautklinik Heidelberg, Im Neuenheimer Feld 440, 69120, Heidelberg, Deutschland.
| | - Holger A Haenssle
- Universitätshautklinik Heidelberg, Im Neuenheimer Feld 440, 69120, Heidelberg, Deutschland
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Khristoforova Y, Bratchenko I, Bratchenko L, Moryatov A, Kozlov S, Kaganov O, Zakharov V. Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification. Diagnostics (Basel) 2022; 12:diagnostics12102503. [PMID: 36292192 PMCID: PMC9600416 DOI: 10.3390/diagnostics12102503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/29/2022] Open
Abstract
In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences.
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Affiliation(s)
- Yulia Khristoforova
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
- Correspondence:
| | - Ivan Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
| | - Lyudmila Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
| | - Alexander Moryatov
- Department of Oncology, Samara State Medical University, 89 Chapaevskaya Str., 443099 Samara, Russia
| | - Sergey Kozlov
- Department of Oncology, Samara State Medical University, 89 Chapaevskaya Str., 443099 Samara, Russia
| | - Oleg Kaganov
- Department of Oncology, Samara State Medical University, 89 Chapaevskaya Str., 443099 Samara, Russia
| | - Valery Zakharov
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
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Han SS, Navarrete-Dechent C, Liopyris K, Kim MS, Park GH, Woo SS, Park J, Shin JW, Kim BR, Kim MJ, Donoso F, Villanueva F, Ramirez C, Chang SE, Halpern A, Kim SH, Na JI. The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search. Sci Rep 2022; 12:16260. [PMID: 36171272 PMCID: PMC9519737 DOI: 10.1038/s41598-022-20632-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/15/2022] [Indexed: 11/09/2022] Open
Abstract
Model Dermatology ( https://modelderm.com ; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets. Consecutive images from an Internet skin cancer community ('RD' dataset, 1,282 images posted between 25 January 2020 to 30 July 2021; https://reddit.com/r/melanoma ) were analyzed retrospectively, along with hospital datasets (Edinburgh dataset, 1,300 images; SNU dataset, 2,101 images; TeleDerm dataset, 340 consecutive images). The algorithm's performance was equivalent to that of dermatologists in the curated clinical datasets (Edinburgh and SNU datasets). However, its performance deteriorated in the RD and TeleDerm datasets because of insufficient image quality and the presence of out-of-distribution disorders, respectively. For the RD dataset, the algorithm's Top-1/3 accuracy (39.2%/67.2%) and AUC (0.800) were equivalent to that of general physicians (36.8%/52.9%). It was more accurate than that of the laypersons using random Internet searches (19.2%/24.4%). The Top-1/3 accuracy was affected by inadequate image quality (adequate = 43.2%/71.3% versus inadequate = 32.9%/60.8%), whereas participant performance did not deteriorate (adequate = 35.8%/52.7% vs. inadequate = 38.4%/53.3%). In this report, the algorithm performance was significantly affected by the change of the intended settings, which implies that AI algorithms at dermatologist-level, in-distribution setting, may not be able to show the same level of performance in with out-of-distribution settings.
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Affiliation(s)
- Seung Seog Han
- Department of Dermatology, I Dermatology Clinic, Seoul, Korea.,IDerma Inc., Seoul, Korea
| | - Cristian Navarrete-Dechent
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Konstantinos Liopyris
- Department of Dermatology, University of Athens, Andreas Syggros Hospital of Skin and Venereal Diseases, Athens, Greece
| | - Myoung Shin Kim
- Department of Dermatology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
| | - Gyeong Hun Park
- Department of Dermatology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sang Seok Woo
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeong deong op-gu, Seoul, 07441, Korea
| | - Juhyun Park
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Jung Won Shin
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Bo Ri Kim
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Min Jae Kim
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Francisca Donoso
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Francisco Villanueva
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Ramirez
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Allan Halpern
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Seong Hwan Kim
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeong deong op-gu, Seoul, 07441, Korea.
| | - Jung-Im Na
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea.
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Shen Z, Hu J, Wu H, Chen Z, Wu W, Lin J, Xu Z, Kong J, Lin T. Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study. Lab Invest 2022; 20:409. [PMID: 36068536 PMCID: PMC9450455 DOI: 10.1186/s12967-022-03615-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/25/2022] [Indexed: 02/08/2023]
Abstract
Background With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci. Methods Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field. Results A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, “breast cancer histopathology” “convolutional neural network” and “histopathological image” were identified as the major future research foci. Conclusions AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03615-0.
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Affiliation(s)
- Zefeng Shen
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jintao Hu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Haiyang Wu
- Graduate School of Tianjin Medical University, No. 22 Qixiangtai Road, Tianjin, 300070, China
| | - Zeshi Chen
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Weixia Wu
- Zhujiang Hospital, Southern Medical University, 253 Gongye Road M, Guangzhou, 510282, China
| | - Junyi Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zixin Xu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jianqiu Kong
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.
| | - Tianxin Lin
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.
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Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations. JID INNOVATIONS 2022; 3:100150. [PMID: 36655135 PMCID: PMC9841357 DOI: 10.1016/j.xjidi.2022.100150] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/17/2022] [Accepted: 07/15/2022] [Indexed: 01/21/2023] Open
Abstract
Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.
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