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Papachristou P, Söderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, Anderson CD, Falk M. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. Br J Dermatol 2024; 191:125-133. [PMID: 38234043 DOI: 10.1093/bjd/ljae021] [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: 11/11/2023] [Revised: 01/12/2024] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics. OBJECTIVES To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. METHODS This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data. RESULTS In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. CONCLUSIONS The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.
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Affiliation(s)
- Panagiotis Papachristou
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - My Söderholm
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Ekholmen Primary Healthcare Centre, Region Östergötland, Linköping, Sweden
| | - Jon Pallon
- Department of Clinical Sciences in Malmö, Family Medicine, Lund University, Malmö, Sweden
- Department of Research and Development, Region Kronoberg, Växjö, Sweden
| | - Marina Taloyan
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - Sam Polesie
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Chris D Anderson
- Department of Biomedical and Clinical Sciences, Division of Dermatology and Venereology, Linköping University, Linköping, Sweden
| | - Magnus Falk
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Region Östergötland, Kärna Primary Healthcare Centre, Linköping, Sweden
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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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Fliorent R, Fardman B, Podwojniak A, Javaid K, Tan IJ, Ghani H, Truong TM, Rao B, Heath C. Artificial intelligence in dermatology: advancements and challenges in skin of color. Int J Dermatol 2024; 63:455-461. [PMID: 38444331 DOI: 10.1111/ijd.17076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/13/2024] [Accepted: 01/30/2024] [Indexed: 03/07/2024]
Abstract
Artificial intelligence (AI) uses algorithms and large language models in computers to simulate human-like problem-solving and decision-making. AI programs have recently acquired widespread popularity in the field of dermatology through the application of online tools in the assessment, diagnosis, and treatment of skin conditions. A literature review was conducted using PubMed and Google Scholar analyzing recent literature (from the last 10 years through October 2023) to evaluate current AI programs in use for dermatologic purposes, identifying challenges in this technology when applied to skin of color (SOC), and proposing future steps to enhance the role of AI in dermatologic practice. Challenges surrounding AI and its application to SOC stem from the underrepresentation of SOC in datasets and issues with image quality and standardization. With these existing issues, current AI programs inevitably do worse at identifying lesions in SOC. Additionally, only 30% of the programs identified in this review had data reported on their use in dermatology, specifically in SOC. Significant development of these applications is required for the accurate depiction of darker skin tone images in datasets. More research is warranted in the future to better understand the efficacy of AI in aiding diagnosis and treatment options for SOC patients.
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Affiliation(s)
| | - Brian Fardman
- Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA
| | | | - Kiran Javaid
- Rowan-Virtua School of Osteopathic Medicine, Stratford, NJ, USA
| | - Isabella J Tan
- Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Hira Ghani
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Thu M Truong
- Center for Dermatology, Rutgers Robert Wood Johnson, Somerset, NJ, USA
| | - Babar Rao
- Center for Dermatology, Rutgers Robert Wood Johnson, Somerset, NJ, USA
| | - Candrice Heath
- Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA
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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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Jones N, Nonaka T. Circulating miRNAs as biomarkers for the diagnosis in patients with melanoma: systematic review and meta-analysis. Front Genet 2024; 15:1339357. [PMID: 38419786 PMCID: PMC10899317 DOI: 10.3389/fgene.2024.1339357] [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: 11/16/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Objective: Melanoma is the most aggressive and deadly form of skin cancer, especially at later stages. There is currently no excellent diagnostic test established for the diagnosis of melanoma; however, circulating microRNAs (miRNAs) have shown some promise. We seek to conduct a systematic review and meta-analysis to establish the clinical utility of circulating miRNAs in diagnosing melanoma. Methods: PubMed, Wiley, and Web of Science were searched for studies that determined miRNA sensitivity and specificity in patients with melanoma. The included studies were assessed in Stata, and the sensitivity, specificity, summary receiver operating characteristic (SROC), positive likelihood ratio, negative likelihood ratio, and the area under the SROC curve (AUC) were calculated. Results: 9 studies with 898 melanoma patients were included in the meta-analysis. The circulating miRNAs showed high diagnostic accuracy with a sensitivity of 0.89 (p < 0.001), specificity of 0.85 (p < 0.001), diagnostic odds ratio of 45, and an area under the curve of 0.93. Conclusion: Circulating miRNAs have shown a high diagnostic power in detecting melanoma.
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Affiliation(s)
- Nicholas Jones
- School of Medicine, Louisiana State University Health Shreveport, Shreveport, LA, United States
| | - Taichiro Nonaka
- Department of Cellular Biology and Anatomy, Louisiana State University Health Sciences Center, Shreveport, LA, United States
- Feist-Weiller Cancer Center, Louisiana State University Health Shreveport, Shreveport, LA, United States
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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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Zhu AQ, Wang Q, Shi YL, Ren WW, Cao X, Ren TT, Wang J, Zhang YQ, Sun YK, Chen XW, Lai YX, Ni N, Chen YC, Hu JL, Mou LC, Zhao YJ, Liu YQ, Sun LP, Zhu XX, Xu HX, Guo LH. A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study. EClinicalMedicine 2024; 67:102391. [PMID: 38274117 PMCID: PMC10808933 DOI: 10.1016/j.eclinm.2023.102391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/27/2024] Open
Abstract
Background Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases. Methods Between Jan 10, 2017, and Dec 31, 2020, the DMFN model was trained and validated using 1269 close-ups and 11,852 HFUS images from 1351 skin lesions. The monomodal convolutional neural network (CNN) model was trained and validated with the same close-up images for comparison. Subsequently, we did a prospective and multicenter study in China. Both CNN models were tested prospectively on 422 cases from 4 hospitals and compared with the results from human raters (general practitioners, general dermatologists, and dermatologists specialized in HFUS). The performance of binary classification (benign vs. malignant) and multiclass classification (the specific diagnoses of 17 types of skin diseases) measured by the area under the receiver operating characteristic curve (AUC) were evaluated. This study is registered with www.chictr.org.cn (ChiCTR2300074765). Findings The performance of the DMFN model (AUC, 0.876) was superior to that of the monomodal CNN model (AUC, 0.697) in the binary classification (P = 0.0063), which was also better than that of the general practitioner (AUC, 0.651, P = 0.0025) and general dermatologists (AUC, 0.838; P = 0.0038). By integrating close-up and HFUS images, the DMFN model attained an almost identical performance in comparison to dermatologists (AUC, 0.876 vs. AUC, 0.891; P = 0.0080). For the multiclass classification, the DMFN model (AUC, 0.707) exhibited superior prediction performance compared with general dermatologists (AUC, 0.514; P = 0.0043) and dermatologists specialized in HFUS (AUC, 0.640; P = 0.0083), respectively. Compared to dermatologists specialized in HFUS, the DMFN model showed better or comparable performance in diagnosing 9 of the 17 skin diseases. Interpretation The DMFN model combining analysis of clinical close-up and HFUS images exhibited satisfactory performance in the binary and multiclass classification compared with the dermatologists. It may be a valuable tool for general dermatologists and primary care providers. Funding This work was supported in part by the National Natural Science Foundation of China and the Clinical research project of Shanghai Skin Disease Hospital.
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Affiliation(s)
- An-Qi Zhu
- Department of Medical Ultrasound, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Qiao Wang
- Department of Medical Ultrasound, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Yi-Lei Shi
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Wei-Wei Ren
- Department of Medical Ultrasound, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Xu Cao
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Tian-Tian Ren
- Department of Medical Ultrasound, Ma'anshan People's Hospital, Ma'anshan, China
| | - Jing Wang
- Department of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Ya-Qin Zhang
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Yi-Kang Sun
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Xue-Wen Chen
- Department of Dermatological Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yong-Xian Lai
- Department of Dermatological Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Na Ni
- Department of Dermatological Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu-Chong Chen
- Department of Dermatological Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | | | - Li-Chao Mou
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
| | - Yu-Jing Zhao
- Department of Medical Ultrasound, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ye-Qiang Liu
- Department of Pathology, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Li-Ping Sun
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - Xiao-Xiang Zhu
- Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
| | - Le-Hang Guo
- Department of Medical Ultrasound, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
| | - China Alliance of Multi-Center Clinical Study for Ultrasound (Ultra-Chance)
- Department of Medical Ultrasound, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China
- MedAI Technology (Wuxi) Co., Ltd., Wuxi, China
- Department of Medical Ultrasound, Ma'anshan People's Hospital, Ma'anshan, China
- Department of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China
- Department of Dermatological Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Pathology, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
- Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany
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Gellatly ZS, Lagha IB, Ternov NK, Berry E, Nelson KC, Seiverling EV. The Role of Dermoscopy in Provider-to-Provider Store-and-Forward Dermatology eConsults: A Scoping Review of the Recent Literature. CURRENT DERMATOLOGY REPORTS 2023; 12:169-179. [PMID: 38390375 PMCID: PMC10883069 DOI: 10.1007/s13671-023-00407-7] [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] [Accepted: 09/25/2023] [Indexed: 02/24/2024]
Abstract
Purpose of Review This scoping review maps recent literature on dermatology provider-to-provider asynchronous store-and-forward (SAF) electronic consult (eConsult) platforms with dermoscopy. It offers a descriptive overview, highlighting benefits and challenges. Recent Findings Incorporating dermoscopy into SAF eConsults improves diagnostic accuracy for benign and malignant skin neoplasms. Diagnostic and treatment concordance with traditional face-to-face (FTF) visits is high. SAF eConsults with dermoscopy enhance access to dermatological care by improving triage and reducing wait times for FTF visits. Pediatric patients benefit with improved evaluation of melanocytic and vascular growths. eConsult platforms with dermoscopy serve as a telementoring opportunity for clinicians interested in improving their dermoscopy skills. Summary Adding dermoscopy to SAF eConsults is valuable and results in improved diagnostic accuracy and reduced need for FTF visits. Implementation barriers can be overcome through collaboration between primary care and dermatology. Dermoscopy in SAF eConsults has significant potential for managing skin conditions and reducing the burden caused by unnecessary FTF visit and biopsies.
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Affiliation(s)
| | - Imene B Lagha
- Tufts Medical Center, Department of Dermatology, Boston, MA 02116, USA
| | - Niels Kvorning Ternov
- Department of Plastic Surgery, Herley and Gentofte University Hospital, Copenhagen, Demark
| | - Elizabeth Berry
- OHSU Department of Dermatology Center for Health and Healing, Portland, OR 97239, USA
| | - Kelly C Nelson
- The University of Texas, Department of Dermatology, Division of Internal Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA
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Elmahdy M, Sebro R. A snapshot of artificial intelligence research 2019-2021: is it replacing or assisting physicians? J Am Med Inform Assoc 2023; 30:1552-1557. [PMID: 37279884 PMCID: PMC10436151 DOI: 10.1093/jamia/ocad094] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug Administration (FDA)-approved AI models were used to assist or replace healthcare providers. We find that most AI models published in this time period were intended to assist rather than replace healthcare providers, and that most of the published AI models performed tasks that could not be done by healthcare providers.
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Affiliation(s)
- Mahmoud Elmahdy
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ronnie Sebro
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
- Center for Augmented Intelligence, Mayo Clinic, Jacksonville, Florida, USA
- Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, Florida, USA
- Department of Biostatistics, Center for Quantitative Health Sciences, Jacksonville, Florida, USA
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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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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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12
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Haugsten ER, Vestergaard T, Trettin B. Experiences Regarding Use and Implementation of Artificial Intelligence-Supported Follow-Up of Atypical Moles at a Dermatological Outpatient Clinic: Qualitative Study. JMIR DERMATOLOGY 2023; 6:e44913. [PMID: 37632937 PMCID: PMC10335120 DOI: 10.2196/44913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly used in numerous medical fields. In dermatology, AI can be used in the form of computer-assisted diagnosis (CAD) systems when assessing and diagnosing skin lesions suspicious of melanoma, a potentially lethal skin cancer with rising incidence all over the world. In particular, CAD may be a valuable tool in the follow-up of patients with high risk of developing melanoma, such as patients with multiple atypical moles. One such CAD system, ATBM Master (FotoFinder), can execute total body dermoscopy (TBD). This process comprises automatically photographing a patient´s entire body and then neatly displaying moles on a computer screen, grouped according to their clinical relevance. Proprietary FotoFinder algorithms underlie this organized presentation of moles. In addition, ATBM Master's optional convoluted neural network (CNN)-based Moleanalyzer Pro software can be used to further assess moles and estimate their probability of malignancy. OBJECTIVE Few qualitative studies have been conducted on the implementation of AI-supported procedures in dermatology. Therefore, the purpose of this study was to investigate how health care providers experience the use and implementation of a CAD system like ATBM Master, in particular its TBD module. In this way, the study aimed to elucidate potential barriers to the application of such new technology. METHODS We conducted a thematic analysis based on 2 focus group interviews with 14 doctors and nurses regularly working in an outpatient pigmented lesions clinic. RESULTS Surprisingly, the study revealed that only 3 participants had actual experience using the TBD module. Even so, all participants were able to provide many notions and anticipations about its use, resulting in 3 major themes emerging from the interviews. First, several organizational matters were revealed to be a barrier to consistent use of the ATBM Master's TBD module, namely lack of guidance, time pressure, and insufficient training. Second, the study found that the perceived benefits of TBD were the ability to objectively detect and monitor subtle lesion changes and unbiasedness of the procedure. Imprecise identification of moles, inability to photograph certain areas, and substandard technical aspects were the perceived weaknesses. Lastly, the study found that clinicians were open to use AI-powered technology and that the TBD module was considered a supplementary tool to aid the medical staff, rather than a replacement of the clinician. CONCLUSIONS Demonstrated by how few of the participants had actual experience with the TBD module, this study showed that implementation of new technology does not occur automatically. It highlights the importance of having a strategy for implementation to ensure the optimized application of CAD tools. The study identified areas that could be improved when implementing AI-powered technology, as well as providing insight on how medical staff anticipated and experienced the use of a CAD device in dermatology.
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Affiliation(s)
- Elisabeth Rygvold Haugsten
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Tine Vestergaard
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Bettina Trettin
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
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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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Giansanti D. The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105810. [PMID: 37239537 DOI: 10.3390/ijerph20105810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/27/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023]
Abstract
Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows.
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15
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Ilișanu MA, Moldoveanu F, Moldoveanu A. Multispectral Imaging for Skin Diseases Assessment-State of the Art and Perspectives. SENSORS (BASEL, SWITZERLAND) 2023; 23:3888. [PMID: 37112229 PMCID: PMC10140977 DOI: 10.3390/s23083888] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/30/2023] [Accepted: 04/07/2023] [Indexed: 06/19/2023]
Abstract
Skin optical inspection is an imperative procedure for a suspicious dermal lesion since very early skin cancer detection can guarantee total recovery. Dermoscopy, confocal laser scanning microscopy, optical coherence tomography, multispectral imaging, multiphoton laser imaging, and 3D topography are the most outstanding optical techniques implemented for skin examination. The accuracy of dermatological diagnoses attained by each of those methods is still debatable, and only dermoscopy is frequently used by all dermatologists. Therefore, a comprehensive method for skin analysis has not yet been established. Multispectral imaging (MSI) is based on light-tissue interaction properties due to radiation wavelength variation. An MSI device collects the reflected radiation after illumination of the lesion with light of different wavelengths and provides a set of spectral images. The concentration maps of the main light-absorbing molecules in the skin, the chromophores, can be retrieved using the intensity values from those images, sometimes even for deeper-located tissues, due to interaction with near-infrared light. Recent studies have shown that portable and cost-efficient MSI systems can be used for extracting skin lesion characteristics useful for early melanoma diagnoses. This review aims to describe the efforts that have been made to develop MSI systems for skin lesions evaluation in the last decade. We examined the hardware characteristics of the produced devices and identified the typical structure of an MSI device for dermatology. The analyzed prototypes showed the possibility of improving the specificity of classification between the melanoma and benign nevi. Currently, however, they are rather adjuvants tools for skin lesion assessment, and efforts are needed towards a fully fledged diagnostic MSI device.
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16
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Li G, Wu G, Xu G, Li C, Zhu Z, Ye Y, Zhang H. Pathological image classification via embedded fusion mutual learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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17
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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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Zhang S, Wang Y, Zheng Q, Li J, Huang J, Long X. Artificial intelligence in melanoma: A systematic review. J Cosmet Dermatol 2022; 21:5993-6004. [PMID: 36001057 DOI: 10.1111/jocd.15323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Melanoma accounts for the majority of skin cancer deaths. Artificial intelligence has been applied in many types of cancers, and in melanoma in recent years. However, no systematic review summarized the application of artificial intelligence in melanoma. AIMS This study aims to systematically review previously published articles to explore the application of artificial intelligence in melanoma. MATERIALS & METHODS PubMed database was used to search the eligible publications on August 1, 2020. The query term was "artificial intelligence" and "melanoma." RESULTS A total of 51 articles were included in this review. Artificial intelligence technique is mainly used in the evaluation of dermoscopic images, other image segmentation and processing, and artificial intelligence diagnosis system. DISCUSSION Artificial intelligence is also applied in metastasis prediction, drug response prediction, and prognosis of melanoma. Besides, patients' perspectives of artificial intelligence and collaboration of human and artificial intelligence in melanoma also attracted attention. The query term might not include all articles, and we could not examine the algorithms that were built without publication. CONCLUSION The performance of artificial intelligence in melanoma is satisfactory and the future for potential applications is enormous.
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Affiliation(s)
- Shu Zhang
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yuanzhuo Wang
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Qingyue Zheng
- Department of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiarui Li
- Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiuzuo Huang
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiao Long
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Han SS, Navarrete-Dechent C, Liopyris K, Kim MS, Park GH, Woo SS, Park J, Shin JW, Kim BR, Kim MJ, Donoso F, Villanueva F, Ramirez C, Chang SE, Halpern A, Kim SH, Na JI. The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search. Sci Rep 2022; 12:16260. [PMID: 36171272 PMCID: PMC9519737 DOI: 10.1038/s41598-022-20632-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/15/2022] [Indexed: 11/09/2022] Open
Abstract
Model Dermatology ( https://modelderm.com ; Build2021) is a publicly testable neural network that can classify 184 skin disorders. We aimed to investigate whether our algorithm can classify clinical images of an Internet community along with tertiary care center datasets. Consecutive images from an Internet skin cancer community ('RD' dataset, 1,282 images posted between 25 January 2020 to 30 July 2021; https://reddit.com/r/melanoma ) were analyzed retrospectively, along with hospital datasets (Edinburgh dataset, 1,300 images; SNU dataset, 2,101 images; TeleDerm dataset, 340 consecutive images). The algorithm's performance was equivalent to that of dermatologists in the curated clinical datasets (Edinburgh and SNU datasets). However, its performance deteriorated in the RD and TeleDerm datasets because of insufficient image quality and the presence of out-of-distribution disorders, respectively. For the RD dataset, the algorithm's Top-1/3 accuracy (39.2%/67.2%) and AUC (0.800) were equivalent to that of general physicians (36.8%/52.9%). It was more accurate than that of the laypersons using random Internet searches (19.2%/24.4%). The Top-1/3 accuracy was affected by inadequate image quality (adequate = 43.2%/71.3% versus inadequate = 32.9%/60.8%), whereas participant performance did not deteriorate (adequate = 35.8%/52.7% vs. inadequate = 38.4%/53.3%). In this report, the algorithm performance was significantly affected by the change of the intended settings, which implies that AI algorithms at dermatologist-level, in-distribution setting, may not be able to show the same level of performance in with out-of-distribution settings.
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Affiliation(s)
- Seung Seog Han
- Department of Dermatology, I Dermatology Clinic, Seoul, Korea.,IDerma Inc., Seoul, Korea
| | - Cristian Navarrete-Dechent
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Konstantinos Liopyris
- Department of Dermatology, University of Athens, Andreas Syggros Hospital of Skin and Venereal Diseases, Athens, Greece
| | - Myoung Shin Kim
- Department of Dermatology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
| | - Gyeong Hun Park
- Department of Dermatology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sang Seok Woo
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeong deong op-gu, Seoul, 07441, Korea
| | - Juhyun Park
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Jung Won Shin
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Bo Ri Kim
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Min Jae Kim
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea
| | - Francisca Donoso
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Francisco Villanueva
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Ramirez
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Allan Halpern
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Seong Hwan Kim
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeong deong op-gu, Seoul, 07441, Korea.
| | - Jung-Im Na
- Department of Dermatology, Seoul National University Bundang Hospital, 82 Gumi-Ro 173 Beon-Gil, Seongnam, 463-707, Gyeonggi, Korea.
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Maron RC, Hekler A, Haggenmüller S, von Kalle C, Utikal JS, Müller V, Gaiser M, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A, French LE, Heinzerling L, Schlager JG, Ghoreschi K, Schlaak M, Hilke FJ, Poch G, Korsing S, Berking C, Heppt MV, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather JN, Fröhling S, Lipka DB, Krieghoff-Henning E, Brinker TJ. Model soups improve performance of dermoscopic skin cancer classifiers. Eur J Cancer 2022; 173:307-316. [PMID: 35973360 DOI: 10.1016/j.ejca.2022.07.002] [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: 06/09/2022] [Accepted: 07/04/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. OBJECTIVE To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. METHODS We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. RESULTS We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. CONCLUSIONS Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency.
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Affiliation(s)
- Roman C Maron
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Achim Hekler
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sarah Haggenmüller
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), 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, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany; DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
| | - Verena Müller
- Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany; DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
| | - Maria Gaiser
- Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany; DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
| | - Friedegund Meier
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Sarah Hobelsberger
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Frank F Gellrich
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Mildred Sergon
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Axel Hauschild
- Department of Dermatology, University Hospital (UKSH), Kiel, Germany
| | - Lars E French
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany; Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Lucie Heinzerling
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
| | - Justin G Schlager
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
| | - Kamran Ghoreschi
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Max Schlaak
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Franz J Hilke
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gabriela Poch
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sören Korsing
- Department of Dermatology, Venereology and Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Carola Berking
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen - European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Markus V Heppt
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen - European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen - European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Sebastian Haferkamp
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Dirk Schadendorf
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Wiebke Sondermann
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Matthias Goebeler
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Bastian Schilling
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, Würzburg, Germany
| | - Jakob N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel B Lipka
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Sun MD, Kentley J, Wilson BW, Soyer HP, Curiel-Lewandrowski CN, Rotemberg V, Halpern AC. Digital skin imaging applications, part I: Assessment of image acquisition technique features. Skin Res Technol 2022; 28:623-632. [PMID: 35652379 PMCID: PMC9907654 DOI: 10.1111/srt.13163] [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: 12/31/2021] [Accepted: 05/03/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality. MATERIALS AND METHODS Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both). RESULTS Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching. CONCLUSION Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.
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Affiliation(s)
- Mary D Sun
- Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Dermatology Service, Memorial Sloan Kettering, New York, New York, USA
| | | | - Britney W Wilson
- Dermatology Service, Memorial Sloan Kettering, New York, New York, USA.,Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - H Peter Soyer
- Dermatology Research Centre, The University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
| | | | | | - Allan C Halpern
- Dermatology Service, Memorial Sloan Kettering, New York, New York, USA
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- Icahn School of Medicine at Mount Sinai, New York, New York, USA
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22
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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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23
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Aloupogianni E, Ishikawa M, Kobayashi N, Obi T. Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220029VR. [PMID: 35676751 PMCID: PMC9174598 DOI: 10.1117/1.jbo.27.6.060901] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/23/2022] [Indexed: 05/11/2023]
Abstract
SIGNIFICANCE Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level. AIM We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue. APPROACH A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified. RESULTS HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems. CONCLUSIONS To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.
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Affiliation(s)
- Eleni Aloupogianni
- Tokyo Institute of Technology, Department of Information and Communication Engineering, Tokyo, Japan
- Address all correspondence to Eleni Aloupogianni,
| | - Masahiro Ishikawa
- Saitama Medical University, Faculty of Health and Medical Care, Saitama, Japan
| | - Naoki Kobayashi
- Saitama Medical University, Faculty of Health and Medical Care, Saitama, Japan
| | - Takashi Obi
- Tokyo Institute of Technology, Department of Information and Communication Engineering, Tokyo, Japan
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
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24
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Han SS, Kim YJ, Moon IJ, Jung JM, Lee MY, Lee WJ, Won CH, Lee MW, Kim SH, Navarrete-Dechent C, Chang SE. Evaluation of Artificial Intelligence-assisted Diagnosis of Skin Neoplasms - a single-center, paralleled, unmasked, randomized controlled trial. J Invest Dermatol 2022; 142:2353-2362.e2. [PMID: 35183551 DOI: 10.1016/j.jid.2022.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/26/2022] [Accepted: 02/08/2022] [Indexed: 11/24/2022]
Abstract
A randomized trial (KCT0005614; https://cris.nih.go.kr) was conducted in a tertiary care institute in South Korea, to validate whether artificial intelligence (AI) could augment the accuracy of non-expert physicians in the real-world settings which included diverse out-of-distribution conditions. Four non-dermatology trainees and four dermatology residents examined the randomly allocated patients with skin lesions suspicious of skin cancer with or without the real-time assistance of AI algorithm (https://b2020.modelderm.com#world; convolutional neural networks). Using 576 consecutive cases (Fitzpatrick skin phototypes III or IV) with suspicious lesions out of the initial 603 recruitments, the accuracy of the AI-assisted group (n=295, 53.9%) was significantly higher than those of the Unaided group (n=281, 43.8%; P=0.019). The augmentation was more significant from 54.7% (n=150) to 30.7% (n=138; P<0.0001) in the non-dermatology trainees who had the least experience in dermatology. The augmentation was not significant in the dermatology residents. The algorithm could help the trainees in the AI-assisted group include more differential diagnoses than the Unaided group (2.09 diagnoses versus 1.95; P=0.0005). In this single-center, unmasked, paralleled, randomized controlled trial, the multiclass AI algorithm augmented the diagnostic accuracy of non-expert physicians in dermatology.
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Affiliation(s)
- Seung Seog Han
- Department of Dermatology, I Dermatology, Clinic, Seoul, Korea; IDerma, Inc., Seoul, Korea
| | - Young Jae Kim
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Ik Jun Moon
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Joon Min Jung
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Mi Young Lee
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Woo Jin Lee
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Chong Hyun Won
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Mi Woo Lee
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Seong Hwan Kim
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Cristian Navarrete-Dechent
- Department of Dermatology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
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25
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Computergestützte Techniken als Entscheidungshilfe bei der Erkennung von Melanomen. AKTUELLE DERMATOLOGIE 2021. [DOI: 10.1055/a-1580-3581] [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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Pathania YS, Apalla Z, Salerni G, Patil A, Grabbe S, Goldust M. Non-invasive diagnostic techniques in pigmentary skin disorders and skin cancer. J Cosmet Dermatol 2021; 21:444-450. [PMID: 34724325 DOI: 10.1111/jocd.14547] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/04/2021] [Accepted: 10/11/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Diagnosis of pigmentary skin disorders, pre-cancerous and cancerous skin diseases is traditionally relied on visual assessment. The most widely applied invasive diagnostic technique is the skin biopsy. There have been significant technological advances in non-invasive diagnostic methods for skin disorders. OBJECTIVE The objective of this article is to discuss different non-invasive diagnostic modalities, used in the diagnosis of pigmentary skin disorders and cutaneous cancers. METHODS Comprehensive literature search was performed to screen articles related to non-invasive diagnostic techniques in pigmentary skin disorders and cutaneous cancers. Articles published in journals indexed in PubMed were searched along with those in Google Scholar. Clinical trials, review articles, case series, case reports and other relevant articles were considered for review. References of relevant articles were also considered for review. RESULTS Dermoscopy and ultrasonography were the only non-invasive diagnostic and imaging techniques available to dermatologists for many years. The advent of computed tomography (CT) and magnetic resonance imaging (MRI) augmented the visualization of deeper structures. Confocal laser microscopy (CLM) and reflectance spectrophotometers have showed promising results in the non-invasive detection of pigmented lesions. Optical coherence tomography (OCT), electrical impedance spectroscopy (EIS), multispectral imaging, high frequency ultrasonography (HFUS) and adhesive patch biopsy aid in the accurate diagnosis of benign, as well as neoplastic skin diseases. CONCLUSION There have been significant advancements in non-invasive methods for diagnosis of dermatological diseases. These techniques can be repeatedly used in a comfort manner for the patient, and may offer an objective way to follow the course of a disease.
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Affiliation(s)
- Yashdeep Singh Pathania
- Department of Dermatology, Venereology and Leprology, All India Institute of Medical Sciences, Jodhpur, India
| | - Zoe Apalla
- Second Dermatology Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Gabriel Salerni
- Department of Dermatology, Hospital Provincial del Centenario de Rosario-Universidad Nacional de Rosario, Rosario, Argentina
| | - Anant Patil
- Department of Pharmacology, Dr. DY Patil Medical College, Navi Mumbai, India
| | - Stephan Grabbe
- Department of Dermatology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Mohamad Goldust
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
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27
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A benchmark for neural network robustness in skin cancer classification. Eur J Cancer 2021; 155:191-199. [PMID: 34388516 DOI: 10.1016/j.ejca.2021.06.047] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/18/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. OBJECTIVE The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. METHODS Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. RESULTS The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. CONCLUSIONS This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.
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28
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Pai VV, Pai RB. Artificial intelligence in dermatology and healthcare: An overview. Indian J Dermatol Venereol Leprol 2021; 87:457-467. [PMID: 34114421 DOI: 10.25259/ijdvl_518_19] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.
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Affiliation(s)
| | - Rohini Bhat Pai
- Department of Anaesthesiology, Goa Medical College, Bambolim, Goa, India
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Blundo A, Cignoni A, Banfi T, Ciuti G. Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis. Front Med (Lausanne) 2021; 8:637069. [PMID: 33968951 PMCID: PMC8103840 DOI: 10.3389/fmed.2021.637069] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/17/2021] [Indexed: 11/24/2022] Open
Abstract
Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8-93.2%) and specificity (85.2%, 95%CI 84.9-85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3-93.1%; specificity 65.2%, 55-74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.
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Affiliation(s)
- Alessia Blundo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Arianna Cignoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Tommaso Banfi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
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30
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Nguyen J, Ting S, Paul E, Smith AL, Watts CG, Kelly J, Cust AE, Mar V. Diagnostic tools used for melanoma: A survey of Australian general practitioners and dermatologists. Australas J Dermatol 2021; 62:300-309. [PMID: 33860932 DOI: 10.1111/ajd.13595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/16/2021] [Accepted: 02/22/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND/OBJECTIVE Diagnostic tools such as dermoscopy, sequential digital dermoscopy imaging (SDDI), total body photography (TBP) and automated diagnostic tools are available to assist in early melanoma diagnosis. The use, accessibility and barriers of dermoscopy have been well studied; however, there are few similar studies regarding SDDI, TBP and automated diagnostic tools. We aim to understand the use of these diagnostic aids amongst Australian general practitioners (GPs) and dermatologists. METHODS Between June 2019 and January 2020, GPs and dermatologists across Australia were invited to participate in an online survey. Surveys were distributed through GP and dermatology organisations. RESULTS A total of 227 survey responses were received, 175 from GPs and 52 from dermatologists. Amongst GPs, 44.6% worked in a skin cancer clinic. Dermoscopy was used at least occasionally by 98.9% of all GPs. SDDI was used by 93.6% of skin cancer GPs, 80.8% of dermatologists and 45.3% of generalist GPs. TBP was used or recommended by 77.1% of generalist GPs, 82.3% of skin cancer GPs and 86.5% of dermatologists. The most common barriers to the use of TBP were cost, limited accessibility, poor patient compliance, and time required for both patients and doctors. Very few clinicians reported using automated diagnostic tools. There was an interest in future diagnostic aids for melanoma in 88% of GPs and dermatologists. CONCLUSION Dermoscopy, SDDI and TBP were commonly used by responding Australian skin cancer GPs and dermatologists in this survey. Automated diagnostic tools were not reported to be used routinely. Several barriers were identified for use of TBP.
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Affiliation(s)
- Jennifer Nguyen
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia.,School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Sarajane Ting
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia.,The Royal Australian College of General Practitioners, East Melbourne, Victoria, Australia
| | - Eldho Paul
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Andrea L Smith
- Macquarie University, Sydney, New South Wales, Australia
| | - Caroline G Watts
- The Sydney School of Public Health and Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia.,Kirby Institute, UNSW, Sydney, New South Wales, Australia
| | - John Kelly
- The Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Anne E Cust
- The Sydney School of Public Health and Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Victoria Mar
- Victorian Melanoma Service, Alfred Health, Melbourne, Victoria, Australia.,School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Sabour S. Comment on "The use of noninvasive imaging techniques in the diagnosis of melanoma: A prospective diagnostic accuracy study". J Am Acad Dermatol 2020; 85:e85. [PMID: 32592883 DOI: 10.1016/j.jaad.2020.06.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 06/08/2020] [Indexed: 11/19/2022]
Affiliation(s)
- Siamak Sabour
- Department of Clinical Epidemiology, School of Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran; Safety Promotions and Injury Prevention Research Centre, Shahid Beheshti University of Medical Sciences, Tehran, Islamic Republic of Iran.
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32
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Marchetti MA, Liopyris K, Navarrete-Dechent C. Net benefit and decision curve analysis of competing diagnostic strategies for cutaneous melanoma. J Am Acad Dermatol 2020; 85:e87-e88. [PMID: 32387669 DOI: 10.1016/j.jaad.2020.04.170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 04/22/2020] [Indexed: 11/24/2022]
Affiliation(s)
- Michael A Marchetti
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Konstantinos Liopyris
- Department of Dermatology, University of Athens, Andreas Syggros Hospital of Skin and Venereal Diseases, Greece
| | - Cristian Navarrete-Dechent
- Department of Dermatology, University of Athens, Andreas Syggros Hospital of Skin and Venereal Diseases, Greece; Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago
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