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Azzopardi E, Boyce D, Azzopardi E, Sadideen H, Mosahebi A. Unveiling the language of scars: A patient-centric themed framework for comprehensive scar morphology. Burns 2024; 50:1269-1276. [PMID: 38480059 DOI: 10.1016/j.burns.2024.02.006] [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: 08/13/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Scarring, a pervasive issue spanning across medical disciplines, lacks a comprehensive terminology for effective communication, patient engagement, and outcome assessment. Existing scar classification systems are constrained by specific pathologies, physician-centric features, and inadequately account for emerging technologies. This study refrains from proposing yet another classification system and instead revisits the foundational language of scar morphology through a theme analysis of primary patient complaints. METHOD Data encompassing five years of a high-volume scar practice was analysed. Primary complaints were aggregated into collective descriptors and further organized into theme domains. The resulting hierarchical map of presenting complaints revealed five key domains: Loss of Function, Contour, Texture, Vector, and Colour Presenting complaints were codified into 42 items, which were then categorised into 14 collective descriptor terms. The latter were in turn organised into five overarching themes. RESULT Loss of Function, accounting for 10% of primary concerns, signifies reduced function attributed solely to the scar. Contour, encompassing 41% of concerns, pertains to scar height, shape, and depth. Texture, representing 12% of concerns, denotes tactile variations such as hardness, roughness, and moisture. Vector, comprising 13% of concerns, refers to scar tissue tension and associated distortions. Colour, the concern in 24% of cases, encompasses variations in pigmentation, vascularity, and exogenous pigments. DISCUSSION Standardized terminology enhances patient care, communication, and research. This study underscores the fundamental question of "what bothers the patient," reviving a patient-centred approach to scar management. By prioritizing themes based on patient complaints, this study innovatively integrates function, aesthetics, and patient experience. In conclusion, this study pioneers a paradigm shift in scar management by presenting a patient-driven theme framework that offers a common language for healthcare professionals and patients. Embracing this language harmonizes scar treatment, fosters innovation, and transforms scars from silent reminders into stories of resilience and healing.
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Affiliation(s)
- Ernest Azzopardi
- London Welbeck Hospital, UK; Consultant Surgeon in Private Practice, UK; University of Malta, Faculty of Medicine and Surgery, UK; University College London, Division of Surgery and Interventional Science, UK; Skinsurgeon Laser Suite, Malta.
| | - Dean Boyce
- Swansea Bay University Local Health Board, UK
| | - Elayne Azzopardi
- Department of Speech and Language Therapy, Department of Health Malta, UK
| | - Hazim Sadideen
- Consultant Surgeon in Private Practice, UK; Imperial College, London, UK
| | - Afshin Mosahebi
- Consultant Surgeon in Private Practice, UK; University College London, Division of Surgery and Interventional Science, UK; Royal Free Hospital Hampstead, UK
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2
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Youn S, Ki MR, Abdelhamid MAA, Pack SP. Biomimetic Materials for Skin Tissue Regeneration and Electronic Skin. Biomimetics (Basel) 2024; 9:278. [PMID: 38786488 PMCID: PMC11117890 DOI: 10.3390/biomimetics9050278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Biomimetic materials have become a promising alternative in the field of tissue engineering and regenerative medicine to address critical challenges in wound healing and skin regeneration. Skin-mimetic materials have enormous potential to improve wound healing outcomes and enable innovative diagnostic and sensor applications. Human skin, with its complex structure and diverse functions, serves as an excellent model for designing biomaterials. Creating effective wound coverings requires mimicking the unique extracellular matrix composition, mechanical properties, and biochemical cues. Additionally, integrating electronic functionality into these materials presents exciting possibilities for real-time monitoring, diagnostics, and personalized healthcare. This review examines biomimetic skin materials and their role in regenerative wound healing, as well as their integration with electronic skin technologies. It discusses recent advances, challenges, and future directions in this rapidly evolving field.
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Affiliation(s)
- Sol Youn
- Department of Biotechnology and Bioinformatics, Korea University, Sejong-Ro 2511, Sejong 30019, Republic of Korea; (S.Y.); (M.A.A.A.)
| | - Mi-Ran Ki
- Department of Biotechnology and Bioinformatics, Korea University, Sejong-Ro 2511, Sejong 30019, Republic of Korea; (S.Y.); (M.A.A.A.)
- Institute of Industrial Technology, Korea University, Sejong-Ro 2511, Sejong 30019, Republic of Korea
| | - Mohamed A. A. Abdelhamid
- Department of Biotechnology and Bioinformatics, Korea University, Sejong-Ro 2511, Sejong 30019, Republic of Korea; (S.Y.); (M.A.A.A.)
- Department of Botany and Microbiology, Faculty of Science, Minia University, Minia 61519, Egypt
| | - Seung-Pil Pack
- Department of Biotechnology and Bioinformatics, Korea University, Sejong-Ro 2511, Sejong 30019, Republic of Korea; (S.Y.); (M.A.A.A.)
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3
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Goessinger EV, Cerminara SE, Mueller AM, Gottfrois P, Huber S, Amaral M, Wenz F, Kostner L, Weiss L, Kunz M, Maul JT, Wespi S, Broman E, Kaufmann S, Patpanathapillai V, Treyer I, Navarini AA, Maul LV. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence. J Eur Acad Dermatol Venereol 2024; 38:945-953. [PMID: 38158385 DOI: 10.1111/jdv.19777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 10/23/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations affect CNN robustness. OBJECTIVES To investigate the consistency of melanoma risk assessment by two commercially available CNNs to help formulate recommendations for current clinical use. METHODS A comparative cohort study was conducted from January to July 2022 at the Department of Dermatology, University Hospital Basel. Five dermoscopic images of 116 different lesions on the torso of 66 patients were captured consecutively by the same operator without deliberate rotation. Classification was performed by two CNNs (CNN-1/CNN-2). Lesions were divided into four subgroups based on their initial risk scoring and clinical dignity assessment. Reliability was assessed by variation and intraclass correlation coefficients. Excisions were performed for melanoma suspicion or two consecutively elevated CNN risk scores, and benign lesions were confirmed by expert consensus (n = 3). RESULTS 117 repeated image series of 116 melanocytic lesions (2 melanomas, 16 dysplastic naevi, 29 naevi, 1 solar lentigo, 1 suspicious and 67 benign) were classified. CNN-1 demonstrated superior measurement repeatability for clinically benign lesions with an initial malignant risk score (mean variation coefficient (mvc): CNN-1: 49.5(±34.3)%; CNN-2: 71.4(±22.5)%; p = 0.03), while CNN-2 outperformed for clinically benign lesions with benign scoring (mvc: CNN-1: 49.7(±22.7)%; CNN-2: 23.8(±29.3)%; p = 0.002). Both systems exhibited lowest score consistency for lesions with an initial malignant risk score and benign assessment. In this context, averaging three initial risk scores achieved highest sensitivity of dignity assessment (CNN-1: 94%; CNN-2: 89%). Intraclass correlation coefficients indicated 'moderate'-to-'good' reliability for both systems (CNN-1: 0.80, 95% CI:0.71-0.87, p < 0.001; CNN-2: 0.67, 95% CI:0.55-0.77, p < 0.001). CONCLUSIONS Potential user-induced image changes can significantly influence CNN classification. For clinical application, we recommend using the average of three initial risk scores. Furthermore, we advocate for CNN robustness optimization by cross-validation with repeated image sets. TRIAL REGISTRATION ClinicalTrials.gov (NCT04605822).
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Affiliation(s)
- E V Goessinger
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - S E Cerminara
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - A M Mueller
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - P Gottfrois
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - S Huber
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - M Amaral
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - F Wenz
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - L Kostner
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - L Weiss
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - M Kunz
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - J-T Maul
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - S Wespi
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - E Broman
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - S Kaufmann
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - V Patpanathapillai
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - I Treyer
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - A A Navarini
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - L V Maul
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
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4
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Winkler JK, Kommoss KS, Toberer F, Enk A, Maul LV, Navarini AA, Hudson J, Salerni G, Rosenberger A, Haenssle HA. Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance. Eur J Cancer 2024; 202:114026. [PMID: 38547776 DOI: 10.1016/j.ejca.2024.114026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 04/21/2024]
Abstract
IMPORTANCE Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire skin surface. Artificial intelligence-based systems are increasingly applied for automated lesion detection and diagnosis. DESIGN AND PATIENTS In this prospective observational international multicentre study experienced dermatologists performed skin cancer screenings and identified clinically relevant melanocytic lesions (CRML, requiring biopsy or observation). Additionally, patients received 2D automated total body mapping (ATBM) with automated lesion detection (ATBM master, Fotofinder Systems GmbH). Primary endpoint was the percentage of CRML detected by the bodyscan software. Secondary endpoints included the percentage of correctly identified "new" and "changed" lesions during follow-up examinations. RESULTS At baseline, dermatologists identified 1075 CRML in 236 patients and 999 CRML (92.9%) were also detected by the automated software. During follow-up examinations dermatologists identified 334 CRMLs in 55 patients, with 323 (96.7%) also being detected by ATBM with automated lesions detection. Moreover, all new (n = 13) or changed CRML (n = 24) during follow-up were detected by the software. Average time requirements per baseline examination was 14.1 min (95% CI [12.8-15.5]). Subgroup analysis of undetected lesions revealed either technical (e.g. covering by clothing, hair) or lesion-specific reasons (e.g. hypopigmentation, palmoplantar sites). CONCLUSIONS ATBM with lesion detection software correctly detected the vast majority of CRML and new or changed CRML during follow-up examinations in a favourable amount of time. Our prospective international study underlines that automated lesion detection in TBP images is feasible, which is of relevance for developing AI-based skin cancer screenings.
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Affiliation(s)
- Julia K Winkler
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
| | | | - Ferdinand Toberer
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Alexander Enk
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
| | - Lara V Maul
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | | | - Jeremy Hudson
- North Queensland Skin Centre, Townsville, Queensland, Australia
| | - Gabriel Salerni
- Department of Dermatology, Hospital Provincial del Centenario de Rosario- Universidad Nacional de Rosario, Rosario, Argentina
| | - Albert Rosenberger
- Institute of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
| | - Holger A Haenssle
- Department of Dermatology, University of Heidelberg, Heidelberg, Germany
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5
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Gaube S, Biebl I, Engelmann MKM, Kleine AK, Lermer E. Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist. Soc Sci Med 2024; 349:116871. [PMID: 38640741 DOI: 10.1016/j.socscimed.2024.116871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/26/2024] [Accepted: 04/04/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND AND AIM Skin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screening offer a potential solution to aid self-examinations; however, their uptake is low. Therefore, the aim of this research was to examine provider and user characteristics influencing people's decisions to seek skin cancer screening performed by a mHealth app or a dermatologist. METHODS Two forced-choice conjoint experiments with Nmain = 1591 and Nreplication = 308 participants from the United States were conducted online to investigate preferences for screening providers. In addition to the provider type (mHealth app vs dermatologist), the following provider attributes were manipulated: costs, expertise, privacy policy, and result details. Subsequently, a questionnaire assessed various user characteristics, including demographics, attitudes toward AI technology and medical mistrust. RESULTS Outcomes were consistent across the two studies. The provider type was the most influential factor, with the dermatologist being selected more often than the mHealth app. Cost, expertise, and privacy policy also significantly impacted decisions. Demographic subgroup analyses showed rather consistent preference trends across various age, gender, and ethnicity groups. Individuals with greater medical mistrust were more inclined to choose the mHealth app. Trust, accuracy, and quality ratings were higher for the dermatologist, whether selected or not. CONCLUSION Our results offer valuable insights for technology developers, healthcare providers, and policymakers, contributing to unlocking the potential of skin cancer screening apps in bridging healthcare gaps in underserved communities.
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Affiliation(s)
- Susanne Gaube
- UCL Global Business School for Health, University College London, UCL East - Marshgate, 7 Sidings St, London, E20 2AE, United Kingdom.
| | - Isabell Biebl
- Center for Leadership and People Management, Department of Psychology, LMU Munich, Geschwister-Scholl-Platz 1, 80539, Munich, Germany
| | | | - Anne-Kathrin Kleine
- Center for Leadership and People Management, Department of Psychology, LMU Munich, Geschwister-Scholl-Platz 1, 80539, Munich, Germany
| | - Eva Lermer
- Center for Leadership and People Management, Department of Psychology, LMU Munich, Geschwister-Scholl-Platz 1, 80539, Munich, Germany; Department of Business Psychology, Technical University of Applied Sciences Augsburg, An der Hochschule 1, 86161, Augsburg, Germany
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6
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Krakowski I, Kim J, Cai ZR, Daneshjou R, Lapins J, Eriksson H, Lykou A, Linos E. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:78. [PMID: 38594408 PMCID: PMC11004168 DOI: 10.1038/s41746-024-01031-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/05/2024] [Indexed: 04/11/2024] Open
Abstract
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
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Affiliation(s)
- Isabelle Krakowski
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Jiyeong Kim
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Zhuo Ran Cai
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Roxana Daneshjou
- Department of Dermatology, Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Jan Lapins
- Department of Dermatology, Theme Inflammation, Karolinska University Hospital, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Unit of Head-Neck-, Lung- and Skin Cancer, Skin Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Anastasia Lykou
- Department of Education, University of Nicosia, Nicosia, Cyprus
| | - Eleni Linos
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA.
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7
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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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8
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Wei ML, Tada M, So A, Torres R. Artificial intelligence and skin cancer. Front Med (Lausanne) 2024; 11:1331895. [PMID: 38566925 PMCID: PMC10985205 DOI: 10.3389/fmed.2024.1331895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.
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Affiliation(s)
- Maria L. Wei
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
| | - Mikio Tada
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, United States
| | - Alexandra So
- School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Rodrigo Torres
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
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9
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Brancaccio G, Balato A, Malvehy J, Puig S, Argenziano G, Kittler H. Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check. J Invest Dermatol 2024; 144:492-499. [PMID: 37978982 DOI: 10.1016/j.jid.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/08/2023] [Accepted: 10/01/2023] [Indexed: 11/19/2023]
Abstract
The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.
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Affiliation(s)
| | - Anna Balato
- Dermatology Unit, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | | | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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10
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Goessinger EV, Niederfeilner JC, Cerminara S, Maul JT, Kostner L, Kunz M, Huber S, Koral E, Habermacher L, Sabato G, Tadic A, Zimmermann C, Navarini A, Maul LV. Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study. J Eur Acad Dermatol Venereol 2024. [PMID: 38411348 DOI: 10.1111/jdv.19905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/22/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting. OBJECTIVES To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence. METHODS A prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high-risk of developing melanoma, with resected or advanced disease) and 8 dermatologists. Patients underwent skin cancer screening by a dermatologist with subsequent 2D and 3D total-body photography (TBP). Any suspicious and all melanocytic skin lesions ≥3 mm were imaged with digital dermoscopes and classified by corresponding convolutional neural networks (CNNs). Excisions were performed based on dermatologist's melanoma suspicion, study-defined elevated CNN risk-scores and/or melanoma suspicion by AuI. Subsequently, all patients and dermatologists were surveyed about their experience using questionnaires, including quantification of patient's safety sense following different examinations (subjective safety score (SSS): 0-10). RESULTS Most patients believed AI could improve diagnostic performance (95.5%, n = 192/201). In total, 83.4% preferred AuI-based skin cancer screening compared to examination by AI or dermatologist alone (3D-TBP: 61.3%; 2D-TBP: 22.1%, n = 199). Regarding SSS, AuI induced a significantly higher feeling of safety than AI (mean-SSS (mSSS): 9.5 vs. 7.7, p < 0.0001) or dermatologist screening alone (mSSS: 9.5 vs. 9.1, p = 0.001). Most dermatologists expressed high trust in AI examination results (3D-TBP: 90.2%; 2D-TBP: 96.1%, n = 205). In 68.3% of the examinations, dermatologists felt that diagnostic accuracy improved through additional AI-assessment (n = 140/205). Especially beginners (<2 years' dermoscopic experience; 61.8%, n = 94/152) felt AI facilitated their clinical work compared to experts (>5 years' dermoscopic experience; 20.9%, n = 9/43). Contrarily, in divergent risk assessments, only 1.5% of dermatologists trusted a benign CNN-classification more than personal malignancy suspicion (n = 3/205). CONCLUSIONS While patients already prefer AuI with 3D-TBP for melanoma recognition, dermatologists continue to rely largely on their own decision-making despite high confidence in AI-results. TRIAL REGISTRATION ClinicalTrials.gov (NCT04605822).
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Affiliation(s)
- Elisabeth Victoria Goessinger
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | | | - Sara Cerminara
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Julia-Tatjana Maul
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Lisa Kostner
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Michael Kunz
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Stephanie Huber
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - Emrah Koral
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
| | - Lea Habermacher
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Gianna Sabato
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Andrea Tadic
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | | | - Alexander Navarini
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Lara Valeska Maul
- Department of Dermatology, University Hospital Basel, Basel, Switzerland
- Faculty of Medicine, University of Basel, Basel, Switzerland
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
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11
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Furriel BCRS, Oliveira BD, Prôa R, Paiva JQ, Loureiro RM, Calixto WP, Reis MRC, Giavina-Bianchi M. Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review. Front Med (Lausanne) 2024; 10:1305954. [PMID: 38259845 PMCID: PMC10800812 DOI: 10.3389/fmed.2023.1305954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists. Its adoption requires analysis of efficacy, safety, and ethical considerations, as well as considering the genetic and ethnic diversity of patients. Objective The systematic review aims to examine research on the detection, classification, and assessment of skin cancer images in clinical settings. Methods We conducted a systematic literature search on PubMed, Scopus, Embase, and Web of Science, encompassing studies published until April 4th, 2023. Study selection, data extraction, and critical appraisal were carried out by two independent reviewers. Results were subsequently presented through a narrative synthesis. Results Through the search, 760 studies were identified in four databases, from which only 18 studies were selected, focusing on developing, implementing, and validating systems to detect, diagnose, and classify skin cancer in clinical settings. This review covers descriptive analysis, data scenarios, data processing and techniques, study results and perspectives, and physician diversity, accessibility, and participation. Conclusion The application of artificial intelligence in dermatology has the potential to revolutionize early detection of skin cancer. However, it is imperative to validate and collaborate with healthcare professionals to ensure its clinical effectiveness and safety.
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Affiliation(s)
- Brunna C. R. S. Furriel
- Imaging Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Electrical, Mechanical and Computer Engineering School, Federal University of Goiás, Goiânia, Brazil
- Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goiás, Goiânia, Brazil
| | - Bruno D. Oliveira
- Imaging Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Renata Prôa
- Imaging Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Joselisa Q. Paiva
- Imaging Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Rafael M. Loureiro
- Imaging Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Wesley P. Calixto
- Electrical, Mechanical and Computer Engineering School, Federal University of Goiás, Goiânia, Brazil
- Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goiás, Goiânia, Brazil
| | - Márcio R. C. Reis
- Imaging Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Studies and Researches in Science and Technology Group (GCITE), Federal Institute of Goiás, Goiânia, Brazil
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12
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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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13
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Cerminara SE, Cheng P, Kostner L, Huber S, Kunz M, Maul JT, Böhm JS, Dettwiler CF, Geser A, Jakopović C, Stoffel LM, Peter JK, Levesque M, Navarini AA, Maul LV. Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening? Eur J Cancer 2023; 190:112954. [PMID: 37453242 DOI: 10.1016/j.ejca.2023.112954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/13/2023] [Accepted: 06/17/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Convolutional neural networks (CNNs) have outperformed dermatologists in classifying pigmented skin lesions under artificial conditions. We investigated, for the first time, the performance of three-dimensional (3D) and two-dimensional (2D) CNNs and dermatologists in the early detection of melanoma in a real-world setting. METHODS In this prospective study, 1690 melanocytic lesions in 143 patients with high-risk criteria for melanoma were evaluated by dermatologists, 2D-FotoFinder-ATBM and 3D-Vectra WB360 total body photography (TBP). Excision was based on the dermatologists' dichotomous decision, an elevated CNN risk score (study-specific malignancy cut-off: FotoFinder >0.5, Vectra >5.0) and/or the second dermatologist's assessment with CNN support. The diagnostic accuracy of the 2D and 3D CNN classification was compared with that of the dermatologists and the augmented intelligence based on histopathology and dermatologists' assessment. Secondary end-points included reproducibility of risk scores and naevus counts per patient by medical staff (gold standard) compared to automated 3D and 2D TBP CNN counts. RESULTS The sensitivity, specificity, and receiver operating characteristics area under the curve (ROC-AUC) for risk-score-assessments compared to histopathology of 3D-CNN with 95% confidence intervals (CI) were 90.0%, 64.6% and 0.92 (CI 0.85-1.00), respectively. While dermatologists and augmented intelligence achieved the same sensitivity (90%) and comparable classification ROC-AUC (0.91 [CI 0.80-1.00], 0.88 [CI 0.77-1.00]) with 3D-CNN, their specificity was superior (92.3% and 86.2%, respectively). The 2D-CNN (sensitivity: 70%, specificity: 40%, ROC-AUC: 0.68 [CI 0.46-0.90]) was outperformed by 3D CNN and dermatologists. The 3D-CNN showed a higher correlation coefficient for repeated measurements of 246 lesions (R = 0.89) than the 2D-CNN (R = 0.79). The mean naevus count per patient varied significantly (gold standard: 210 lesions; 3D-CNN: 469; 2D-CNN: 1324; p < 0.0001). CONCLUSIONS Our study emphasises the importance of validating the classification of CNNs in real life. The novel 3D-CNN device outperformed the 2D-CNN and achieved comparable sensitivity with dermatologists. The low specificity of CNNs and the lack of automated counting of TBP nevi currently limit the use of augmented intelligence in clinical practice.
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Affiliation(s)
- Sara E Cerminara
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Phil Cheng
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | - Lisa Kostner
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Stephanie Huber
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Michael Kunz
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Julia-Tatjana Maul
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland; Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Jette S Böhm
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Chiara F Dettwiler
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Anna Geser
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Cécile Jakopović
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Livia M Stoffel
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Jelissa K Peter
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Mitchell Levesque
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | | | - Lara Valeska Maul
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland.
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14
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Grossarth S, Mosley D, Madden C, Ike J, Smith I, Huo Y, Wheless L. Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods. Curr Oncol Rep 2023; 25:635-645. [PMID: 37000340 PMCID: PMC10339689 DOI: 10.1007/s11912-023-01407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/01/2023]
Abstract
PURPOSE OF REVIEW The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma. RECENT FINDINGS Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.
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Affiliation(s)
- Sarah Grossarth
- Quillen College of Medicine, East Tennessee State University, Johnson City, TN, USA
| | | | - Christopher Madden
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- State University of New York Downstate College of Medicine, Brooklyn, NY, USA
| | - Jacqueline Ike
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Meharry Medical College, Nashville, TN, USA
| | - Isabelle Smith
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Yuankai Huo
- Department of Computer Science and Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lee Wheless
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA.
- Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Tennessee Valley Healthcare System VA Medical Center, Nashville, TN, USA.
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15
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Recent Progress in the Diagnosis and Treatment of Melanoma and Other Skin Cancers. Cancers (Basel) 2023; 15:cancers15061824. [PMID: 36980709 PMCID: PMC10046835 DOI: 10.3390/cancers15061824] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/15/2023] [Indexed: 03/22/2023] Open
Abstract
In this Special Issue, the reader will find nine papers regarding recent progress in diagnosis and treatment to optimize the clinical management of melanoma and non-melanoma skin cancer [...]
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16
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Soglia S, Pérez-Anker J, Lobos Guede N, Giavedoni P, Puig S, Malvehy J. Diagnostics Using Non-Invasive Technologies in Dermatological Oncology. Cancers (Basel) 2022; 14:cancers14235886. [PMID: 36497368 PMCID: PMC9738560 DOI: 10.3390/cancers14235886] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
The growing incidence of skin cancer, with its associated mortality and morbidity, has in recent years led to the developing of new non-invasive technologies, which allow an earlier and more accurate diagnosis. Some of these, such as digital photography, 2D and 3D total-body photography and dermoscopy are now widely used and others, such as reflectance confocal microscopy and optical coherence tomography, are limited to a few academic and referral skin cancer centers because of their cost or the long training period required. Health care professionals involved in the treatment of patients with skin cancer need to know the implications and benefits of new non-invasive technologies for dermatological oncology. In this article we review the characteristics and usability of the main diagnostic imaging methods available today.
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Affiliation(s)
- Simone Soglia
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, 08001 Barcelona, Spain
- Department of Dermatology, University of Brescia, 25121 Brescia, Italy
| | - Javiera Pérez-Anker
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, 08001 Barcelona, Spain
- Correspondence: ; Tel.: +34-932-275-400
| | - Nelson Lobos Guede
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, 08001 Barcelona, Spain
| | - Priscila Giavedoni
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, 08001 Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, 08001 Barcelona, Spain
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, 08001 Barcelona, Spain
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