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Arjun KP, Kumar KS, Dhanaraj RK, Ravi V, Kumar TG. Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model. Microsc Res Tech 2024; 87:1789-1809. [PMID: 38515433 DOI: 10.1002/jemt.24559] [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: 05/20/2023] [Revised: 01/13/2024] [Accepted: 03/14/2024] [Indexed: 03/23/2024]
Abstract
Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.
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Affiliation(s)
- K P Arjun
- Department of Computer Science and Engineering, GITAM University, Bangalore, India
| | - K Sampath Kumar
- Department of Computer Science and Engineering, AMET University, Chennai, India
| | - Rajesh Kumar Dhanaraj
- Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
| | - T Ganesh Kumar
- School of Computing Science and Engineering, Galgotias University, Greater Noida, India
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Wen D, Soltan A, Trucco E, Matin RN. From data to diagnosis: skin cancer image datasets for artificial intelligence. Clin Exp Dermatol 2024; 49:675-685. [PMID: 38549552 DOI: 10.1093/ced/llae112] [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: 11/28/2023] [Revised: 02/11/2024] [Accepted: 03/25/2024] [Indexed: 06/26/2024]
Abstract
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritized groups in existing datasets are highlighted, which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques that may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.
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Affiliation(s)
- David Wen
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK
| | - Andrew Soltan
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford Cancer and Haematology Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Emanuele Trucco
- VAMPIRE Project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Rubeta N Matin
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Artificial Intelligence Working Party Group, British Association of Dermatologists, London, UK
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Bolick NL, Geller AC. Epidemiology and Screening for Melanoma. Hematol Oncol Clin North Am 2024:S0889-8588(24)00052-2. [PMID: 38908959 DOI: 10.1016/j.hoc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
Melanoma is the most commonly fatal type of skin cancer, and it is an important and growing public health problem in the United States and worldwide. Fortunately, incidence rates are decreasing in young people, stabilizing in middle-aged people, and increasing in older individuals. Herein, the authors further describe trends in melanoma incidence and mortality, review the literature on risk factors, and provide an up-to-date assessment of population-wide screening and new technology being utilized in melanoma screening.
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Affiliation(s)
- Nicole L Bolick
- Department of Dermatology, University of New Mexico School of Medicine, MSC08 4720 1 UNM, Albuquerque, NM 87131, USA
| | - Alan C Geller
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Kresge Building, Room 718, 677 Huntington Avenue, Boston, MA 02115, USA.
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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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Munuswamy Selvaraj K, Gnanagurusubbiah S, Roby Roy RR, John Peter JH, Balu S. Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics. Curr Probl Cancer 2024; 49:101077. [PMID: 38480028 DOI: 10.1016/j.currproblcancer.2024.101077] [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: 08/21/2023] [Revised: 01/27/2024] [Accepted: 02/28/2024] [Indexed: 04/29/2024]
Abstract
Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learning-based approach for accurate skin lesion classification, addressing challenges such as limited data availability, class imbalance, and noise. Modern deep neural network designs, such as ResNeXt101, SeResNeXt101, ResNet152V2, DenseNet201, GoogLeNet, and Xception, which are used in the study and ze optimised using the SGD technique. The dataset comprises diverse skin lesion images from the HAM10000 and ISIC datasets. Noise and artifacts are tackled using image inpainting, and data augmentation techniques enhance training sample diversity. The ensemble technique is utilized, creating both average and weighted average ensemble models. Grid search optimizes model weight distribution. The individual models exhibit varying performance, with metrics including recall, precision, F1 score, and MCC. The "Average ensemble model" achieves harmonious balance, emphasizing precision, F1 score, and recall, yielding high performance. The "Weighted ensemble model" capitalizes on individual models' strengths, showcasing heightened precision and MCC, yielding outstanding performance. The ensemble models consistently outperform individual models, with the average ensemble model attaining a macro-average ROC-AUC score of 96 % and the weighted ensemble model achieving a macro-average ROC-AUC score of 97 %. This research demonstrates the efficacy of ensemble techniques in significantly improving skin lesion classification accuracy. By harnessing the strengths of individual models and addressing their limitations, the ensemble models exhibit robust and reliable performance across various metrics. The findings underscore the potential of ensemble techniques in enhancing medical diagnostics and contributing to improved patient outcomes in skin lesion diagnosis.
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Affiliation(s)
- Kavitha Munuswamy Selvaraj
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, RSM Nagar, Chennai, Tamil Nadu, India.
| | - Sumathy Gnanagurusubbiah
- Department of Computational Intelligence, SRM Institute of Science and Technology, kattankulathur, Tamil Nadu, India
| | - Reena Roy Roby Roy
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Jasmine Hephzipah John Peter
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, RSM Nagar, Chennai, Tamil Nadu, India
| | - Sarala Balu
- Department of Electronics and Communication Engineering, R.M.K. Engineering College, RSM Nagar, Chennai, Tamil Nadu, India
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Helenason J, Ekström C, Falk M, Papachristou P. Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care - a mixed method study. Scand J Prim Health Care 2024; 42:51-60. [PMID: 37982736 PMCID: PMC10851794 DOI: 10.1080/02813432.2023.2283190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/08/2023] [Indexed: 11/21/2023] Open
Abstract
Objective: Skin examination to detect cutaneous melanomas is commonly performed in primary care. In recent years, clinical decision support systems (CDSS) based on artificial intelligence (AI) have been introduced within several diagnostic fields.Setting: This study employs a variety of qualitative and quantitative methodologies to investigate the feasibility of an AI-based CDSS to detect cutaneous melanoma in primary care.Subjects and Design: Fifteen primary care physicians (PCPs) underwent near-live simulations using the CDSS on a simulated patient, and subsequent individual semi-structured interviews were explored with a hybrid thematic analysis approach. Additionally, twenty-five PCPs performed a reader study (diagnostic assessment on the basis of image interpretation) of 18 dermoscopic images, both with and without help from AI, investigating the value of adding AI support to a PCPs decision. Perceived instrument usability was rated on the System Usability Scale (SUS).Results: From the interviews, the importance of trust in the CDSS emerged as a central concern. Scientific evidence supporting sufficient diagnostic accuracy of the CDSS was expressed as an important factor that could increase trust. Access to AI decision support when evaluating dermoscopic images proved valuable as it formally increased the physician's diagnostic accuracy. A mean SUS score of 84.8, corresponding to 'good' usability, was measured.Conclusion: AI-based CDSS might play an important future role in cutaneous melanoma diagnostics, provided sufficient evidence of diagnostic accuracy and usability supporting its trustworthiness among the users.
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Affiliation(s)
| | | | - Magnus Falk
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Panagiotis Papachristou
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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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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Sangers TE, Kittler H, Blum A, Braun RP, Barata C, Cartocci A, Combalia M, Esdaile B, Guitera P, Haenssle HA, Kvorning N, Lallas A, Navarrete-Dechent C, Navarini AA, Podlipnik S, Rotemberg V, Soyer HP, Tognetti L, Tschandl P, Malvehy J. Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease. J Eur Acad Dermatol Venereol 2024; 38:22-30. [PMID: 37766502 DOI: 10.1111/jdv.19521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. OBJECTIVE This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection. METHODS An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. RESULTS Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. CONCLUSIONS The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.
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Affiliation(s)
- Tobias E Sangers
- Department of Dermatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Andreas Blum
- Public, Private and Teaching Practice of Dermatology Konstanz, Konstanz, Germany
| | - Ralph P Braun
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - Catarina Barata
- Institute for Systems and Robotics, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Ben Esdaile
- Department of Dermatology, Whittington NHS Trust, London, UK
| | - Pascale Guitera
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Holger A Haenssle
- Department of Dermatology, Heidelberg University Medical Center, Heidelberg, Germany
| | - Niels Kvorning
- Department of Plastic Surgery, Herlev Hospital, Herlev, Denmark
| | - Aimilios Lallas
- First Department of Dermatology, Faculty of Health Sciences, School of Medicine, Aristotle University, Thessaloniki, Greece
| | - Cristian Navarrete-Dechent
- Melanoma and Skin Cancer Unit, Department of Dermatology, Escuela de Medicina, Pontifica Universidad Catolica de Chile, Santiago, Chile
| | - Alexander A Navarini
- Department of Dermatology and Department of Biomedical Engineering, University Hospital of Basel, Basel, Switzerland
| | - Sebastian Podlipnik
- Department of Dermatology, Hospital Clínic, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Veronica Rotemberg
- Division of Dermatology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - H Peter Soyer
- Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Linda Tognetti
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
| | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
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Patel RH, Foltz EA, Witkowski A, Ludzik J. Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review. Cancers (Basel) 2023; 15:4694. [PMID: 37835388 PMCID: PMC10571810 DOI: 10.3390/cancers15194694] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/05/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. OBJECTIVE The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. METHODS A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. RESULTS We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. CONCLUSIONS Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.
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Affiliation(s)
- Raj H. Patel
- Edward Via College of Osteopathic Medicine, VCOM-Louisiana, 4408 Bon Aire Dr, Monroe, LA 71203, USA
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
| | - Emilie A. Foltz
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Alexander Witkowski
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
| | - Joanna Ludzik
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
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10
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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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11
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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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12
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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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13
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Boswell Z, Verga JU, Mackle J, Guerrero-Vazquez K, Thomas OP, Cray J, Wolf BJ, Choo YM, Croot P, Hamann MT, Hardiman G. In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses. Infect Drug Resist 2023; 16:2321-2338. [PMID: 37155475 PMCID: PMC10122865 DOI: 10.2147/idr.s395203] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 03/16/2023] [Indexed: 05/10/2023] Open
Abstract
The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.
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Affiliation(s)
- Zachary Boswell
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
| | - Jacopo Umberto Verga
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
- Genomic Data Science, University of Galway, Galway, Ireland
| | - James Mackle
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
| | | | - Olivier P Thomas
- School of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, H91TK33Ireland
| | - James Cray
- Department of Biomedical Education and Anatomy, College of Medicine and Division of Biosciences, College of Dentistry, Ohio State University, Columbus, OH, USA
| | - Bethany J Wolf
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Yeun-Mun Choo
- Department of Chemistry, University of Malaya, Kuala Lumpur, Malaysia
| | - Peter Croot
- Irish Centre for Research in Applied Geoscience, Earth and Ocean Sciences and Ryan Institute, School of Natural Sciences, University of Galway, Galway, Ireland
| | - Mark T Hamann
- Departments of Drug Discovery and Biomedical Sciences and Public Health, Colleges of Pharmacy and Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Gary Hardiman
- School of Biological Sciences and Institute for Global Security, Queen's University, Belfast, Northern Ireland, UK
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
- Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
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14
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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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15
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Shi Z, Zhu J, Yu L, Li X, Li J, Chen H, Chen L. A Two-Stage End-to-End Deep Learning Framework for Pathologic Examination in Skin Tumor Diagnosis. THE AMERICAN JOURNAL OF PATHOLOGY 2023:S0002-9440(23)00059-7. [PMID: 36868466 DOI: 10.1016/j.ajpath.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 01/10/2023] [Accepted: 02/10/2023] [Indexed: 03/05/2023]
Abstract
Neurofibromas (NFs), Bowen disease (BD), and seborrheic keratosis (SK) are common skin tumors. Pathologic examination is the golden standard for diagnosis of these tumors. Current pathologic diagnosis is mainly based on the observation of naked eyes under microscope, which is laborious and time-consuming. Digitization of pathology brings the opportunity for artificial intelligence technology to improve the efficiency of diagnosis. This research aims to develop an end-to-end extendable framework for the diagnosis of skin tumor based on pathologic slide images. NF, BD, and SK were selected as target skin tumors. A two-stage skin cancer diagnosis framework is proposed in this article, which consists of two parts: patches-wise diagnosis and slide-wise diagnosis. Patches-wise diagnosis compares different convolutional neural networks to extract features and distinguish categories from patches generated in whole slide images. Slide-wise diagnosis combines attention graph gated network model prediction with post-processing algorithm. This approach can fuse information from feature-embedding learning and domain knowledge to draw a conclusion. Training, validation, and testing were performed on NF, BD, SK, and negative samples. Accuracy and receiver operating characteristic curves were used to evaluate the classification performance. This study investigated the feasibility of skin tumor diagnosis in pathologic image and may be the first time that deep learning is applied to address these three kinds of tumor diagnosis in skin pathology.
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Affiliation(s)
- Zhijie Shi
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingyi Zhu
- Academy of Engineering and Technology, Fudan University, Shanghai, China
| | - Liheng Yu
- Academy of Engineering and Technology, Fudan University, Shanghai, China
| | - Xiaopeng Li
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Jiaxin Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China; University of Chinese Academy of Sciences, Beijing, China
| | - Huyan Chen
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China.
| | - Lianjun Chen
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China.
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16
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Oprescu F, Fjaagesund S, Hardy M, Jones E. Transforming Primary Care: Developing Health Precincts as Models for Sustainable Integrated Community-Based Healthcare. Healthcare (Basel) 2023; 11:healthcare11050673. [PMID: 36900681 PMCID: PMC10000379 DOI: 10.3390/healthcare11050673] [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: 12/31/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 03/03/2023] Open
Abstract
Holistic healthcare precincts are an emerging service model to address the growing health service demands of ageing consumers and an increasing prevalence of chronic diseases. In Australia and similar countries with universal publicly funded Medicare systems, the first point of access to healthcare is provided by general medical practitioners. This case report focuses on successful components of a private, integrated, patient-centred primary care model located in a low socioeconomic population in North Brisbane, Queensland. Successful components included a focus on sustainability, general practice as an anchor tenant in the health precinct, the integration of multiple services, team-based care for shared clinical services, flexible expansion options, the use of MedTech, support for small businesses and a cluster structure. The Morayfield Health Precinct (MHP) offers appropriate, safe and individualised healthcare to residents across their life continuum. Its success was built on a foundation of pre-planning, to ensure the design/build, anchor tenant and collaborative ecosystem were sustainable in the long term. MHP planning was based on an adaptation of the WHO-IPCC framework supporting true patient-centred, integrated care. Its shared vision and collaborative care are supported by its internal governance structure, tenant selection, established and emerging referral networks and partnerships. Evidence-based and informed care is further supported by internal and external research and education partnerships.
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17
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MINAGAWA A, NAKAMURA A, KOGA H, SOTA T. Diagnostic Utility of a Revised Formula-based Melanoma Discrimination Index for Nail Streaks in Dermoscopic Practice. Acta Derm Venereol 2022; 102:adv00829. [PMID: 36472366 PMCID: PMC9811303 DOI: 10.2340/actadv.v102.4351] [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] [Indexed: 12/12/2022] Open
Abstract
Abstract is missing (Short communication)
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Affiliation(s)
- Akane MINAGAWA
- Department of Dermatology, Shinshu University School of Medicine, Matsumoto
| | - Atsushi NAKAMURA
- Department of Electrical Engineering and Bioscience, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Hiroshi KOGA
- Department of Dermatology, Shinshu University School of Medicine, Matsumoto
| | - Takayuki SOTA
- Department of Electrical Engineering and Bioscience, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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18
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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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19
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Dubuc A, Zitouni A, Thomas C, Kémoun P, Cousty S, Monsarrat P, Laurencin S. Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study. J Clin Med 2022; 11:jcm11216596. [PMID: 36362822 PMCID: PMC9654969 DOI: 10.3390/jcm11216596] [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: 09/27/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.
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Affiliation(s)
- Antoine Dubuc
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France
| | - Anissa Zitouni
- Oral Surgery and Oral Medicine Department, CHU Limoges, 87000 Limoges, France
| | - Charlotte Thomas
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- InCOMM, I2MC, UMR 1297, Paul Sabatier University, 31062 Toulouse, France
| | - Philippe Kémoun
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, CHU de Toulouse, 31300 Toulouse, France
| | - Sarah Cousty
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- LAPLACE, UMR 5213 CNRS, Paul Sabatier University, 31062 Toulouse, France
| | - Paul Monsarrat
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, CHU de Toulouse, 31300 Toulouse, France
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
| | - Sara Laurencin
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France
- Correspondence:
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20
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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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21
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Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images. J Fungi (Basel) 2022; 8:jof8090912. [PMID: 36135637 PMCID: PMC9504700 DOI: 10.3390/jof8090912] [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: 07/17/2022] [Revised: 08/18/2022] [Accepted: 08/25/2022] [Indexed: 11/20/2022] Open
Abstract
Background: Onychomycosis numbers among the most common fungal infections in humans affecting finger- or toenails. Histology remains a frequently applied screening technique to diagnose onychomycosis. Screening slides for fungal elements can be time-consuming for pathologists, and sensitivity in cases with low amounts of fungi remains a concern. Convolutional neural networks (CNNs) have revolutionized image classification in recent years. The goal of our project was to evaluate if a U-NET-based segmentation approach as a subcategory of CNNs can be applied to detect fungal elements on digitized histologic sections of human nail specimens and to compare it with the performance of 11 board-certified dermatopathologists. Methods: In total, 664 corresponding H&E- and PAS-stained histologic whole-slide images (WSIs) of human nail plates from four different laboratories were digitized. Histologic structures were manually annotated. A U-NET image segmentation model was trained for binary segmentation on the dataset generated by annotated slides. Results: The U-NET algorithm detected 90.5% of WSIs with fungi, demonstrating a comparable sensitivity with that of the 11 board-certified dermatopathologists (sensitivity of 89.2%). Conclusions: Our results demonstrate that machine-learning-based algorithms applied to real-world clinical cases can produce comparable sensitivities to human pathologists. Our established U-NET may be used as a supportive diagnostic tool to preselect possible slides with fungal elements. Slides where fungal elements are indicated by our U-NET should be reevaluated by the pathologist to confirm or refute the diagnosis of onychomycosis.
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22
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Deep Learning Approaches for Automatic Localization in Medical Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6347307. [PMID: 35814554 PMCID: PMC9259335 DOI: 10.1155/2022/6347307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 05/23/2022] [Indexed: 12/21/2022]
Abstract
Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.
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23
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Jones OT, Matin RN, van der Schaar M, Prathivadi Bhayankaram K, Ranmuthu CKI, Islam MS, Behiyat D, Boscott R, Calanzani N, Emery J, Williams HC, Walter FM. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. THE LANCET DIGITAL HEALTH 2022; 4:e466-e476. [DOI: 10.1016/s2589-7500(22)00023-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 11/29/2021] [Accepted: 01/28/2022] [Indexed: 12/17/2022]
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24
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Skin cancer diagnosis using convolutional neural networks for smartphone images: A comparative study. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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25
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Stiff KM, Franklin MJ, Zhou Y, Madabhushi A, Knackstedt TJ. Artificial Intelligence and Melanoma: A Comprehensive Review of Clinical, Dermoscopic, and Histologic Applications. Pigment Cell Melanoma Res 2022; 35:203-211. [PMID: 35038383 DOI: 10.1111/pcmr.13027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/24/2021] [Accepted: 01/09/2022] [Indexed: 11/30/2022]
Abstract
Melanoma detection, prognosis, and treatment represent challenging and complex areas of cutaneous oncology with considerable impact on patient outcomes and healthcare economics. Artificial intelligence (AI) applications in these tasks are rapidly developing. Neural networks with increasing levels of sophistication are being implemented in clinical image, dermoscopic image, and histopathologic specimen classification of pigmented lesions. These efforts hold promise of earlier and highly accurate melanoma detection, as well as reliable prognostication and prediction of therapeutic response. Herein, we provide a brief introduction to AI, discuss contemporary investigational applications of AI in melanoma, and summarize challenges encountered with AI.
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Affiliation(s)
| | | | - Yufei Zhou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland
| | - Thomas J Knackstedt
- Department of Dermatology, MetroHealth System, Cleveland.,School of Medicine, Case Western Reserve University, Cleveland
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26
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Callegarin D, Callier P. Enjeux du déploiement de l’intelligence artificielle en santé. ACTUALITES PHARMACEUTIQUES 2021. [DOI: 10.1016/j.actpha.2021.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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27
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Interpretability of a Deep Learning Based Approach for the Classification of Skin Lesions into Main Anatomic Body Sites. Cancers (Basel) 2021; 13:cancers13236048. [PMID: 34885158 PMCID: PMC8657137 DOI: 10.3390/cancers13236048] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 12/15/2022] Open
Abstract
Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks, including VGG19, ResNet50, Xception, DenseNet121, and EfficientNetB0, to calculate the features with an adjusted and densely connected classifier. Furthermore, we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.
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Winkler JK, Tschandl P, Toberer F, Sies K, Fink C, Enk A, Kittler H, Haenssle HA. Monitoring patients at risk for melanoma: May convolutional neural networks replace the strategy of sequential digital dermoscopy? Eur J Cancer 2021; 160:180-188. [PMID: 34840028 DOI: 10.1016/j.ejca.2021.10.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/06/2021] [Accepted: 10/25/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Sequential digital dermoscopy (SDD) is applied for early melanoma detection by uncovering dynamic changes of monitored lesions. Convolutional neural networks (CNN) are capable of high diagnostic accuracies similar to trained dermatologists. OBJECTIVES To investigate the capability of CNN to correctly classify melanomas originally diagnosed by mere dynamic changes during SDD. METHODS A retrospective cross-sectional study using image quartets of 59 high-risk patients each containing one melanoma diagnosed by dynamic changes during SDD and three nevi (236 lesions). Two validated CNN classified quartets at baseline or after SDD follow-up at the time of melanoma diagnosis. Moreover, baseline quartets were rated by 26 dermatologists. The main outcome was the number of quartets with correct classifications. RESULTS CNN-1 correctly classified 9 (15.3%) and CNN-2 8 (13.6%) of 59 baseline quartets. In baseline images, CNN-1 attained a sensitivity of 25.4% (16.1%-37.8%) and specificity of 92.7% (87.8%-95.7%), whereas CNN-2 of 28.8% (18.8%-41.4%) and 75.7% (68.9%-81.4%). Expectedly, after SDD follow-up CNN more readily detected melanomas resulting in improved sensitivities (CNN-1: 44.1% [32.2%-56.7%]; CNN-2: 49.2% [36.8%-61.6%]). Dermatologists were told that each baseline quartet contained one melanoma, and on average, correctly classified 24 (22-27) of 59 quartets. Correspondingly, accepting a baseline quartet to be appropriately classified whenever the highest malignancy score was assigned to the melanoma within, CNN-1 and CNN-2 correctly classified 28 (47.5%) and 22 (37.3%) of 59 quartets, respectively. CONCLUSIONS The tested CNN could not replace the strategy of SDD. There is a need for CNN capable of integrating information on dynamic changes into analyses.
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Affiliation(s)
- Julia K Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Ferdinand Toberer
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Katharina Sies
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Christine Fink
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Alexander Enk
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Holger A Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
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29
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Whi W, Choi H, Paeng JC, Cheon GJ, Kang KW, Lee DS. Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping. EJNMMI Phys 2021; 8:79. [PMID: 34778923 PMCID: PMC8590988 DOI: 10.1186/s40658-021-00424-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/29/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The whole brain is often covered in [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aimed to develop a fully automated quantitative interpretation pipeline of brain volume from an oncology PET image. METHOD We retrospectively collected 500 oncologic [18F]FDG-PET scans for training and validation of the automated brain extractor. We trained the model for extracting brain volume with two manually drawn bounding boxes on maximal intensity projection images. ResNet-50, a 2-D convolutional neural network (CNN), was used for the model training. The brain volume was automatically extracted using the CNN model and spatially normalized. For validation of the trained model and an application of this automated analytic method, we enrolled 24 subjects with small cell lung cancer (SCLC) and performed voxel-wise two-sample T test for automatic detection of metastatic lesions. RESULT The deep learning-based brain extractor successfully identified the existence of whole-brain volume, with an accuracy of 98% for the validation set. The performance of extracting the brain measured by the intersection-over-union of 3-D bounding boxes was 72.9 ± 12.5% for the validation set. As an example of the application to automatically identify brain abnormality, this approach successfully identified the metastatic lesions in three of the four cases of SCLC patients with brain metastasis. CONCLUSION Based on the deep learning-based model, extraction of the brain volume from whole-body PET was successfully performed. We suggest this fully automated approach could be used for the quantitative analysis of brain metabolic patterns to identify abnormalities during clinical interpretation of oncologic PET studies.
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Affiliation(s)
- Wonseok Whi
- Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Jin Chul Paeng
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Gi Jeong Cheon
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
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30
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Daneshjou R, Smith MP, Sun MD, Rotemberg V, Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review. JAMA Dermatol 2021; 157:1362-1369. [PMID: 34550305 DOI: 10.1001/jamadermatol.2021.3129] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical care, but fair, generalizable algorithms depend on the clinical data on which they are trained and tested. Objective To assess whether data sets used for training diagnostic AI algorithms addressing skin disease are adequately described and to identify potential sources of bias in these data sets. Data Sources In this scoping review, PubMed was used to search for peer-reviewed research articles published between January 1, 2015, and November 1, 2020, with the following paired search terms: deep learning and dermatology, artificial intelligence and dermatology, deep learning and dermatologist, and artificial intelligence and dermatologist. Study Selection Studies that developed or tested an existing deep learning algorithm for triage, diagnosis, or monitoring using clinical or dermoscopic images of skin disease were selected, and the articles were independently reviewed by 2 investigators to verify that they met selection criteria. Consensus Process Data set audit criteria were determined by consensus of all authors after reviewing existing literature to highlight data set transparency and sources of bias. Results A total of 70 unique studies were included. Among these studies, 1 065 291 images were used to develop or test AI algorithms, of which only 257 372 (24.2%) were publicly available. Only 14 studies (20.0%) included descriptions of patient ethnicity or race in at least 1 data set used. Only 7 studies (10.0%) included any information about skin tone in at least 1 data set used. Thirty-six of the 56 studies developing new AI algorithms for cutaneous malignant neoplasms (64.3%) met the gold standard criteria for disease labeling. Public data sets were cited more often than private data sets, suggesting that public data sets contribute more to new development and benchmarks. Conclusions and Relevance This scoping review identified 3 issues in data sets that are used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: (1) sparsity of data set characterization and lack of transparency, (2) nonstandard and unverified disease labels, and (3) inability to fully assess patient diversity used for algorithm development and testing.
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Affiliation(s)
- Roxana Daneshjou
- Stanford Department of Dermatology, Stanford School of Medicine, Redwood City, California.,Stanford Department of Biomedical Data Science, Stanford School of Medicine, Stanford, California
| | - Mary P Smith
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mary D Sun
- currently a medical student at Icahn School of Medicine at Mount Sinai, New York, New York
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Zou
- Department of Electrical Engineering, Stanford University, Stanford, California.,Department of Biomedical Data Science, Stanford University, Stanford, California.,Chan Zuckerberg Biohub, San Francisco, California
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31
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Tschandl P. Risk of Bias and Error From Data Sets Used for Dermatologic Artificial Intelligence. JAMA Dermatol 2021; 157:1271-1273. [PMID: 34550304 DOI: 10.1001/jamadermatol.2021.3128] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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32
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Use of artificial intelligence to predict mean time to delivery following cervical ripening with dinoprostone vaginal insert. Eur J Obstet Gynecol Reprod Biol 2021; 266:1-6. [PMID: 34537667 DOI: 10.1016/j.ejogrb.2021.08.031] [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: 06/15/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To validate a mathematical model to predict the mean time to delivery (TTD) following cervical ripening with dinoprostone vaginal insert (DVI), and assess its impact on the risk of nocturnal deliveries. METHODS We performed a case-control retro-prospective study at Angers University Hospital. In the control group, we retrospectively included 405 patients who underwent cervical ripening with DVI between 01/2015 and 09/2016. Based on the delivery outcomes, we developed a mathematical model that integrates all the factors influencing TTD following cervical ripening with DVI. In the study group, we prospectively included 223 patients who underwent cervical ripening with DVI between 11/2017 and 11/2018. The timing of insertion was calculated using the mathematical model developed in the control group, in order to prevent the occurrence of nocturnal deliveries. RESULTS The calculated mean TTD was significantly shorter than the real mean TTD (21h46 min ± 3h28 min versus 25h38 min ± 12h10 min, p < 0.001), and for 44% of patients, there was at least 10 h difference between the two. The real TTD (25h38 min ± 12H10 min versus 20h39 min ± 10h49, p < 0.001), and the rate of nocturnal deliveries (30.5% versus 21.2%, p = 0.01) were significantly higher in the study group compared to the control group. CONCLUSION The mathematical model did not help predicting TTD following cervical ripening with DVI, and or reducing the number of nocturnal deliveries.
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Kittler H. Evolution of the Clinical, Dermoscopic and Pathologic Diagnosis of Melanoma. Dermatol Pract Concept 2021; 11:e2021163S. [PMID: 34447612 DOI: 10.5826/dpc.11s1a163s] [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: 04/26/2021] [Indexed: 10/31/2022] Open
Abstract
The conventional narrative states that the steadily rising incidence of melanoma among fair-skinned Caucasian populations during the last decades is caused by excessive UV-exposure. There is, however, no doubt that other factors had a significant impact on the rising incidence of melanoma. Pre-1980s the clinical diagnosis of melanoma was based on gross criteria such as ulceration or bleeding. Melanomas were often diagnosed in advanced stages when the prognosis was grim. In the mid-1980s education campaigns such as the propagation of the ABCD criteria, which addressed health care professionals and the public alike, shifted the focus towards early recognition. Dermatoscopy, which became increasingly popular in the mid-1990s, improved the accuracy for the diagnosis of melanoma in comparison to inspection with the unaided eye, especially for flat and small lesions lacking ABCD criteria. At the same time, pathologists began to lower their thresholds, particularly for the diagnosis of melanoma in situ. The melanoma epidemic that followed was mainly driven by an increase in the number of in situ or microinvasive melanomas. In a few decades, the landscape shifted from an undercalling to an overcalling of melanomas, a development that is now met with increased criticism. The gold standard of melanoma diagnosis is still conventional pathology, which is faced with low to moderate interobserver agreement. New insights in the molecular landscape of melanoma did not translate into techniques for the reliable diagnosis of gray zone lesions including small lesions. The aim of this review is to put our current view of melanoma diagnosis in historical context and to provide a narrative synthesis of its evolution. Based on this narrative I will provide suggestions on how to rebuild the trust in melanoma diagnosis accuracy and in the benefit of early recognition.
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Affiliation(s)
- Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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34
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Naik PP. Diagnosis and Management of Lentigo Maligna: Clinical Presentation and Comprehensive Review. J Skin Cancer 2021; 2021:7178305. [PMID: 34350036 PMCID: PMC8328731 DOI: 10.1155/2021/7178305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/10/2021] [Accepted: 07/21/2021] [Indexed: 11/17/2022] Open
Abstract
Lentigo maligna (LM), also known as Hutchinson's melanotic freckle, is a form of in situ melanoma characterized by the proliferation of atypical melanocytes along the basal epidermis in sun-damaged skin. If left untreated, LM will progress to lentigo maligna melanoma (LMM), a form of invasive melanoma with the same prognosis as other forms of invasive melanoma. LM is more common in the elderly, with a peak occurrence between the ages of 65 and 80 years. LM, however, is rarely present on the trunk and extremities. The diagnosis of LM, confirmed by histopathological and biopsy examination, is based on clinical and dermoscopic features. It typically begins as a tan-brown macule or patch, but it can progress to a variegated pigmentation with dark black color or even amelanotic characteristics. The risk factors involved in the LM development include a history of sunburns, lighter skin types, advanced age, history of nonmelanoma skin cancers, and tendency to form solar lentigines. This article explains the clinical presentation of LM, also reviews the available information on the diagnosis and management of LM, and discusses the potential of such information in facilitating the future prospective.
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Affiliation(s)
- Piyu Parth Naik
- Department of Dermatology, Saudi German Hospitals and Clinics, Hessa Street 331 West, Al Barsha 3, Exit 36 Sheikh Zayed Road, Opposite of American School, Dubai, UAE
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Ningrum DNA, Yuan SP, Kung WM, Wu CC, Tzeng IS, Huang CY, Li JYC, Wang YC. Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection. J Multidiscip Healthc 2021; 14:877-885. [PMID: 33907414 PMCID: PMC8071207 DOI: 10.2147/jmdh.s306284] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/25/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient's metadata, but the study is still lacking. OBJECTIVE We want to develop malignant melanoma detection based on dermoscopic images and patient's metadata using an artificial intelligence (AI) model that will work on low-resource devices. METHODS We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient's metadata (CNN+ANN model). RESULTS The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient's metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources. CONCLUSION The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.
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Affiliation(s)
- Dina Nur Anggraini Ningrum
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Public Health Department, Universitas Negeri Semarang, Semarang City, Indonesia
| | - Sheng-Po Yuan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Otorhinolaryngology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Woon-Man Kung
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
| | - Chieh-Chen Wu
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
| | - I-Shiang Tzeng
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
- Department of Statistics, National Taipei University, Taipei, Taiwan
| | - Chu-Ya Huang
- Taiwan College of Healthcare Executives, Taipei, Taiwan
| | - Jack Yu-Chuan Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department Dermatology, Wan Fang Hospital, Taipei, Taiwan
- Taipei Medical University Research Center of Cancer Translational Medicine, Taipei, Taiwan
| | - Yao-Chin Wang
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
- Department of Emergency Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan
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36
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Dessinioti C, Geller AC, Whiteman DC, Garbe C, Grob JJ, Kelly JW, Scolyer RA, Rawson RV, Lallas A, Pellacani G, Stratigos AJ. Not all melanomas are created equal: a review and call for more research into nodular melanoma. Br J Dermatol 2021; 185:700-710. [PMID: 33864261 DOI: 10.1111/bjd.20388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2021] [Indexed: 12/23/2022]
Abstract
Among the histogenic subtypes of melanoma, nodular melanoma (NM) is the major contributor for thicker and fatal melanomas and it has been associated with melanoma-specific death in thin tumours, highlighting an important subgroup of 'aggressive thin' melanomas. This review provides a synthesis of the distinct characteristics of NM, with respect to epidemiology and risk factors, clinical presentation, histopathology, molecular and dermoscopic aspects, and screening practices. The real challenges are to find better biomarkers of aggressiveness and to know whether the control of such aggressive melanomas can be influenced by targeted interventions such as early detection, drug interventions and preventive strategies.
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Affiliation(s)
- C Dessinioti
- First Department of Dermatology-Venereology, National and Kapodistrian University of Athens, Athens, Greece
| | - A C Geller
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - D C Whiteman
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - C Garbe
- Center for Dermatooncology, Department of Dermatology, Eberhard Karls University, Tuebingen, Germany
| | - J J Grob
- Department of Dermatology and Skin Cancers, APHM Timone Hospital Aix-Marseille University, Marseille, France
| | - J W Kelly
- Victorian Melanoma Service, Alfred Hospital, Melbourne, Australia
| | - R A Scolyer
- Melanoma Institute Australia, Sydney, NSW, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.,Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW, Australia
| | - R V Rawson
- Melanoma Institute Australia, Sydney, NSW, Australia.,Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.,Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital and NSW Health Pathology, Sydney, NSW, Australia
| | - A Lallas
- First Department of Dermatology, Aristotle University, Thessaloniki, Greece
| | - G Pellacani
- Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - A J Stratigos
- First Department of Dermatology-Venereology, National and Kapodistrian University of Athens, Athens, Greece
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37
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Höhn J, Krieghoff-Henning E, Jutzi TB, von Kalle C, Utikal JS, Meier F, Gellrich FF, Hobelsberger S, Hauschild A, Schlager JG, French L, Heinzerling L, Schlaak M, Ghoreschi K, Hilke FJ, Poch G, Kutzner H, Heppt MV, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Goebeler M, Hekler A, Fröhling S, Lipka DB, Kather JN, Krahl D, Ferrara G, Haggenmüller S, Brinker TJ. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification. Eur J Cancer 2021; 149:94-101. [PMID: 33838393 DOI: 10.1016/j.ejca.2021.02.032] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/12/2021] [Accepted: 02/13/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. OBJECTIVES We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone. METHODS We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier. RESULTS The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%. CONCLUSION In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.
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Affiliation(s)
- Julia Höhn
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Tanja B Jutzi
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Christof von Kalle
- Department of Clinical-Translational Sciences, Charité University Medicine and Berlin Institute of Health (BIH), Berlin, Germany
| | - Jochen S Utikal
- Department of Dermatology, Heidelberg University, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Friedegund Meier
- Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität, Dresden, Germany
| | - Frank F Gellrich
- Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität, Dresden, Germany
| | - Sarah Hobelsberger
- Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität, Dresden, Germany
| | - Axel Hauschild
- Department of Dermatology, University Hospital of Kiel, Germany
| | - Justin G Schlager
- Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Lars French
- Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Lucie Heinzerling
- Department of Dermatology and Allergology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Max Schlaak
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kamran Ghoreschi
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Franz J Hilke
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gabriela Poch
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Heinz Kutzner
- Dermatopathology Laboratory, Friedrichshafen, Germany
| | - Markus V Heppt
- Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Sebastian Haferkamp
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Wiebke Sondermann
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Dirk Schadendorf
- Department of Dermatology, University Hospital Essen, Essen, Germany
| | - Bastian Schilling
- Department of Dermatology, University Hospital Würzburg, Würzburg, Germany
| | - Matthias Goebeler
- Department of Dermatology, University Hospital Würzburg, Würzburg, Germany
| | - Achim Hekler
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Stefan Fröhling
- Section Translational Cancer Epigenomics, Division Translational Medical Oncology, German Cancer Research Center (DKFZ) & National Center for Tumor Diseases (NCT), Heidelberg, 69120, Germany
| | - Daniel B Lipka
- Section Translational Cancer Epigenomics, Division Translational Medical Oncology, German Cancer Research Center (DKFZ) & National Center for Tumor Diseases (NCT), Heidelberg, 69120, Germany
| | - Jakob N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Dieter Krahl
- Private Laboratory of Dermatohistopathology, Mönchhofstraße 52, Heidelberg, 69120, Germany
| | - Gerardo Ferrara
- Anatomic Pathology Unit, Macerata General Hospital, Macerata, Italy
| | - Sarah Haggenmüller
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.
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Winkler JK, Sies K, Fink C, Toberer F, Enk A, Abassi MS, Fuchs T, Haenssle HA. Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition. Eur J Cancer 2021; 145:146-154. [PMID: 33465706 DOI: 10.1016/j.ejca.2020.12.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Studies systematically unravelling possible causes for false diagnoses of deep learning convolutional neural networks (CNNs) are scarce, yet needed before broader application. OBJECTIVES The objective of the study was to investigate whether scale bars in dermoscopic images are associated with the diagnostic accuracy of a market-approved CNN. METHODS This cross-sectional analysis applied a CNN trained with more than 150,000 images (Moleanalyzer-pro®, FotoFinder Systems Inc., Bad Birnbach, Germany) to investigate seven dermoscopic image sets depicting the same 130 melanocytic lesions (107 nevi, 23 melanomas) without or with digitally superimposed scale bars of different manufacturers. Sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for the CNN's binary classification of images with or without superimposed scale bars were assessed. RESULTS Six dermoscopic image sets with different scale bars and one control set without scale bars (overall 910 images) were submitted to CNN analysis. In images without scale bars, the CNN attained a sensitivity [95% confidence interval] of 87.0% [67.9%-95.5%] and a specificity of 87.9% [80.3%-92.8%]. ROC AUC was 0.953 [0.914-0.992]. Scale bars were not associated with significant changes in sensitivity (range 87%-95.7%, all p ≥ 1.0). However, four scale bars induced a decrease of the CNN's specificity (range 0%-43.9%, all p < 0.001). Moreover, ROC AUC was significantly reduced by two scale bars (range 0.520-0.848, both p ≤ 0.042). CONCLUSIONS Superimposed scale bars in dermoscopic images may impair the CNN's diagnostic accuracy, mostly by increasing the rate of the false-positive diagnoses. We recommend avoiding scale bars in images intended for CNN analysis unless specific measures counteracting effects are implemented. CLINICAL TRIAL NUMBER This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; URL: https://www.drks.de/drks_web/).
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Affiliation(s)
- Julia K Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Katharina Sies
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Christine Fink
- 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
| | - Mohamed S Abassi
- Department of Research and Development, FotoFinder Systems GmbH, Bad Birnbach, Germany
| | - Tobias Fuchs
- Department of Research and Development, FotoFinder Systems GmbH, Bad Birnbach, Germany
| | - Holger A Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
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39
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Iqbal I, Younus M, Walayat K, Kakar MU, Ma J. Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Comput Med Imaging Graph 2021; 88:101843. [PMID: 33445062 DOI: 10.1016/j.compmedimag.2020.101843] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 11/13/2020] [Accepted: 12/11/2020] [Indexed: 10/22/2022]
Abstract
As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life.
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Affiliation(s)
- Imran Iqbal
- Department of Information and Computational Sciences, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, People's Republic of China.
| | - Muhammad Younus
- State Key Laboratory of Membrane Biology and Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine and Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China.
| | - Khuram Walayat
- Faculty of Engineering Technology, Department of Thermal and Fluid Engineering, University of Twente, Enschede, 7500 AE, Netherlands.
| | - Mohib Ullah Kakar
- Beijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
| | - Jinwen Ma
- Department of Information and Computational Sciences, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, People's Republic of China.
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40
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Tognetti L, Bonechi S, Andreini P, Bianchini M, Scarselli F, Cevenini G, Moscarella E, Farnetani F, Longo C, Lallas A, Carrera C, Puig S, Tiodorovic D, Perrot JL, Pellacani G, Argenziano G, Cinotti E, Cataldo G, Balistreri A, Mecocci A, Gori M, Rubegni P, Cartocci A. A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi. J Dermatol Sci 2020; 101:115-122. [PMID: 33358096 DOI: 10.1016/j.jdermsci.2020.11.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM). OBJECTIVE We aimed to develop a Deep Convolutional Neural Network (DCNN) model able to support dermatologists in the classification and management of atypical melanocytic skin lesions (aMSL). METHODS A training set (630 images), a validation set (135) and a testing set (214) were derived from the idScore dataset of 979 challenging aMSL cases in which the dermoscopic image is integrated with clinical data (age, sex, body site and diameter) and associated with histological data. A DCNN_aMSL architecture was designed and then trained on both dermoscopic images of aMSL and the clinical/anamnestic data, resulting in the integrated "iDCNN_aMSL" model. Responses of 111 dermatologists with different experience levels on both aMSL classification (intuitive diagnosis) and management decisions (no/long follow-up; short follow-up; excision/preventive excision) were compared with the DCNNs models. RESULTS In the lesion classification study, the iDCNN_aMSL achieved the best accuracy, reaching an AUC = 90.3 %, SE = 86.5 % and SP = 73.6 %, compared to DCNN_aMSL (SE = 89.2 %, SP = 65.7 %) and intuitive diagnosis of dermatologists (SE = 77.0 %; SP = 61.4 %). CONCLUSIONS The iDCNN_aMSL proved to be the best support tool for management decisions reducing the ratio of inappropriate excision. The proposed iDCNN_aMSL model can represent a valid support for dermatologists in discriminating AN from EM with high accuracy and for medical decision making by reducing their rates of inappropriate excisions.
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Affiliation(s)
- Linda Tognetti
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy.
| | - Simone Bonechi
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy; Department of Economy Engineering Society and Buisiness, Tuscia University, Viterbo, Italy
| | - Paolo Andreini
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Monica Bianchini
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Franco Scarselli
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Gabriele Cevenini
- Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy
| | - Elvira Moscarella
- Dermatology Unit, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Francesca Farnetani
- Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | - Caterina Longo
- Centro Oncologico ad Alta Tecnologia Diagnostica, Azienda Unità Sanitaria Locale, IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Aimilios Lallas
- First Department of Dermatology, Aristotle University, Thessaloniki, Greece
| | - Cristina Carrera
- Melanoma Unit, Department of Dermatology, University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, University of Barcelona, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Department of Dermatology, University of Barcelona, Barcelona, Spain
| | | | - Jean Luc Perrot
- Dermatology Unit, University Hospital of St-Etienne, Saint Etienne, France
| | - Giovanni Pellacani
- Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Elisa Cinotti
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy
| | - Gennaro Cataldo
- Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy
| | - Alberto Balistreri
- Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy
| | - Alessandro Mecocci
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Marco Gori
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Pietro Rubegni
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy
| | - Alessandra Cartocci
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy; Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy
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Iznardo H, Garcia-Melendo C, Yélamos O. Lentigo Maligna: Clinical Presentation and Appropriate Management. Clin Cosmet Investig Dermatol 2020; 13:837-855. [PMID: 33223843 PMCID: PMC7671473 DOI: 10.2147/ccid.s224738] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 10/15/2020] [Indexed: 01/12/2023]
Abstract
Lentigo maligna (LM) is a type of melanoma in situ that has distinctive characteristics regarding epidemiology, risk factors and clinical features. In addition, LM has a potential to progress to an invasive tumor with potentially aggressive behavior: lentigo maligna melanoma (LMM). Overall, LM has a very good prognosis, whereas LMM has the same prognosis as other invasive melanomas with similar Breslow thickness. LM/LMM represents a challenging entity not only regarding the diagnosis but also regarding the management. Diagnostic criteria are not well established, and there is an overlap of clinical, dermoscopic and pathological features with other benign pigmented skin lesions such as lentigines, pigmented actinic keratoses or macular seborrheic keratoses. LM/LMM's common appearance within photodamaged skin makes lesion border identification difficult. Wide excisions are often required, but since LM/LMM typically appears on cosmetically sensitive areas such as the face, sometimes large excisions are not possible nor desirable. In this sense, specialized approaches have been developed such as margin-controlled surgery or image-guided treatment using reflectance confocal microscopy. Other treatments for LM such as cryosurgery, imiquimod, radiotherapy or photodynamic therapy have been proposed, although recurrence/persistence is common. The current manuscript reviews extensively the published data regarding the diagnosis, treatment and management of both complex entities LM and LMM.
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Affiliation(s)
- Helena Iznardo
- Dermatology Service, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Cristina Garcia-Melendo
- Dermatology Service, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Oriol Yélamos
- Dermatology Service, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Dermatology Service, Centro Médico Teknon - Quirónsalud, Barcelona, Spain
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42
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Abstract
Please add expansion for AL. Melanoma is the most common fatal type of skin cancer and is an important and growing public health problem in the United States, Australia, New Zealand, and Europe. The mortality rate in most of the world has been rising as well, albeit slower than that for incidence. Likely due to the availability of new treatments for stage 4 melanoma, mortality rates in the United States dropped 18% from 2013 to 2016. We further describe trends in melanoma incidence and mortality, review the literature on risk factors, and provide an up-to-date assessment of population-wide screening and some of the inherent concerns.
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Affiliation(s)
- Nicole L Bolick
- Harvard T.H. Chan School of Public Health, Kresge Building Room 718, 677 Huntington Ave, Boston, MA 02115, USA; Department of Internal Medicine, East Carolina University/Vidant Medical Center, 600 Moye Boulevard, MA-350, Greenville, NC 27834, USA
| | - Alan C Geller
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Kresge Building, Room 718, 677 Huntington Avenue, Boston, MA 02115, USA.
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43
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Blum A, Bosch S, Haenssle HA, Fink C, Hofmann-Wellenhof R, Zalaudek I, Kittler H, Tschandl P. [Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice]. Hautarzt 2020; 71:691-698. [PMID: 32720165 DOI: 10.1007/s00105-020-04658-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI) With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.
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Affiliation(s)
- A Blum
- Hautarzt- und Lehrpraxis, Augustinerplatz 7, 78462, Konstanz, Deutschland.
| | - S Bosch
- Hautarztpraxis, Ludwigsburg, Deutschland
| | - H A Haenssle
- Universitäts-Hautklinik Heidelberg, Heidelberg, Deutschland
| | - C Fink
- Universitäts-Hautklinik Heidelberg, Heidelberg, Deutschland
| | - R Hofmann-Wellenhof
- Universitätsklinik für Dermatologie, Medizinische Universität Graz, Graz, Österreich
| | - I Zalaudek
- Dermatology Clinic, University Hospital of Trieste, Hospital Maggiore, Trieste, Italien
| | - H Kittler
- Universitätsklinik für Dermatologie, Medizinische Universität Wien, Wien, Österreich
| | - P Tschandl
- Universitätsklinik für Dermatologie, Medizinische Universität Wien, Wien, Österreich
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Lallas A, Lallas K, Tschandl P, Kittler H, Apalla Z, Longo C, Argenziano G. The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis. J Am Acad Dermatol 2020; 84:381-389. [PMID: 32592885 DOI: 10.1016/j.jaad.2020.06.085] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/16/2020] [Accepted: 06/19/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND A recently introduced dermoscopic method for the diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis and solar lentigo/flat seborrheic keratosis. We term this the inverse approach. OBJECTIVE To determine whether training on the inverse approach increases the diagnostic accuracy of readers compared to classic pattern analysis. METHODS We used clinical and dermoscopic images of histopathologically diagnosed LMs, pigmented actinic keratoses, and solar lentigo/flat seborrheic keratoses. Participants in a dermoscopy masterclass classified the lesions at baseline and after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 timepoints and to that of a trained convolutional neural network. RESULTS The mean sensitivity for LM without training was 51.5%; after training on pattern analysis, it increased to 56.7%; and after learning the inverse approach, it increased to 83.6%. The mean proportions of correct answers at the 3 timepoints were 62.1%, 65.5, and 78.5%. The percentages of readers outperforming the convolutional neural network were 6.4%, 15.4%, and 53.9%, respectively. LIMITATIONS The experimental setting and the inclusion of histopathologically diagnosed lesions only. CONCLUSIONS The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.
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Affiliation(s)
- Aimilios Lallas
- First Department of Dermatology, Aristotle University, Thessaloniki, Greece.
| | | | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Zoe Apalla
- Second Department of Dermatology, Aristotle University, Thessaloniki, Greece
| | - Caterina Longo
- Centro Oncologico ad Alta Tecnologia Diagnostica, Azienda Unità Sanitaria Locale-Istituto di Ricovero e Cura a Carattere Scientifico di Reggio Emilia, Reggio Emilia, Italy; Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
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