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Lyakhova UA, Lyakhov PA. Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects. Comput Biol Med 2024; 178:108742. [PMID: 38875908 DOI: 10.1016/j.compbiomed.2024.108742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 06/16/2024]
Abstract
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.
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Affiliation(s)
- U A Lyakhova
- Department of Mathematical Modeling, North-Caucasus Federal University, 355017, Stavropol, Russia.
| | - P A Lyakhov
- Department of Mathematical Modeling, North-Caucasus Federal University, 355017, Stavropol, Russia; North-Caucasus Center for Mathematical Research, North-Caucasus Federal University, 355017, Stavropol, Russia.
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2
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Salinas MP, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, Rotemberg V, Briones J, Mery D, Navarrete-Dechent C. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. NPJ Digit Med 2024; 7:125. [PMID: 38744955 PMCID: PMC11094047 DOI: 10.1038/s41746-024-01103-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/04/2024] [Indexed: 05/16/2024] Open
Abstract
Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.
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Affiliation(s)
- Maria Paz Salinas
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javiera Sepúlveda
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Leonel Hidalgo
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominga Peirano
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Morel
- Universidad Catolica-Evidence Center, Cochrane Chile Associated Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pablo Uribe
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
- Melanoma and Skin Cancer Unit, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Veronica Rotemberg
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Juan Briones
- Department of Oncology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Domingo Mery
- Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Navarrete-Dechent
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Melanoma and Skin Cancer Unit, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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3
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Viknesh CK, Kumar PN, Seetharaman R, Anitha D. Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique. Diagnostics (Basel) 2023; 13:3313. [PMID: 37958209 PMCID: PMC10649387 DOI: 10.3390/diagnostics13213313] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/15/2023] Open
Abstract
Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.
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Affiliation(s)
- Chandran Kaushik Viknesh
- Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India; (P.N.K.); (R.S.)
| | - Palanisamy Nirmal Kumar
- Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India; (P.N.K.); (R.S.)
| | - Ramasamy Seetharaman
- Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India; (P.N.K.); (R.S.)
| | - Devasahayam Anitha
- Department of Science and Humanities, Karpagam Institute of Technology, Coimbatore 641105, India;
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Kuo KM, Talley PC, Chang CS. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Med Inform Decis Mak 2023; 23:138. [PMID: 37501114 PMCID: PMC10375663 DOI: 10.1186/s12911-023-02229-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/07/2023] [Indexed: 07/29/2023] Open
Abstract
BACKGROUND With rising incidence of skin cancer and relatively increased mortality rates, an improved diagnosis of such a potentially fatal disease is of vital importance. Although frequently curable, it nevertheless places a considerable burden upon healthcare systems. Among the various types of skin cancers, non-melanoma skin cancer is most prevalent. Despite such prevalence and its associated cost, scant proof concerning the diagnostic accuracy via Artificial Intelligence (AI) for non-melanoma skin cancer exists. This study meta-analyzes the diagnostic test accuracy of AI used to diagnose non-melanoma forms of skin cancer, and it identifies potential covariates that account for heterogeneity between extant studies. METHODS Various electronic databases (Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions) were examined to discern eligible studies beginning from March 2022. Those AI studies predictive of non-melanoma skin cancer were included. Summary estimates of sensitivity, specificity, and area under receiver operating characteristic curves were used to evaluate diagnostic accuracy. The revised Quality Assessment of Diagnostic Studies served to assess any risk of bias. RESULTS A literature search produced 39 eligible articles for meta-analysis. The summary sensitivity, specificity, and area under receiver operating characteristic curve of AI for diagnosing non-melanoma skin cancer was 0.78, 0.98, & 0.97, respectively. Skin cancer typology, data sources, cross validation, ensemble models, types of techniques, pre-trained models, and image augmentation became significant covariates accounting for heterogeneity in terms of both sensitivity and/or specificity. CONCLUSIONS Meta-analysis results revealed that AI is predictive of non-melanoma with an acceptable performance, but sensitivity may become improved. Further, ensemble models and pre-trained models are employable to improve true positive rating.
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Affiliation(s)
- Kuang Ming Kuo
- Department of Business Management, National United University, No.1, Miaoli, 360301, Lienda, Taiwan, Republic of China
| | - Paul C Talley
- Department of Applied English, I-Shou University, No. 1, Sec. 1, Syuecheng Rd., Dashu District, 84001, Kaohsiung City, Taiwan, Republic of China
| | - Chao-Sheng Chang
- Department of Occupational Therapy, I-Shou University, No. 1, Yida Rd., Yanchao District, 82445, Kaohsiung City, Taiwan, Republic of China.
- Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan, Republic of China.
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Deniz-Garcia A, Fabelo H, Rodriguez-Almeida AJ, Zamora-Zamorano G, Castro-Fernandez M, Alberiche Ruano MDP, Solvoll T, Granja C, Schopf TR, Callico GM, Soguero-Ruiz C, Wägner AM. Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. J Med Internet Res 2023; 25:e44030. [PMID: 37140973 PMCID: PMC10196903 DOI: 10.2196/44030] [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: 11/09/2022] [Revised: 02/19/2023] [Accepted: 03/10/2023] [Indexed: 03/12/2023] Open
Abstract
The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.
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Affiliation(s)
- Alejandro Deniz-Garcia
- Endocrinology and Nutrition Department, Complejo Hospitalario Universitario Insular Materno Infantil, Las Palmas de Gran Canaria, Spain
| | - Himar Fabelo
- Complejo Hospitalario Universitario Insular - Materno Infantil, Fundación Canaria Instituto de Investigación Sanitaria de Canarias, Las Palmas de Gran Canaria, Spain
- Research Institute for Applied Microelectronics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Antonio J Rodriguez-Almeida
- Research Institute for Applied Microelectronics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Garlene Zamora-Zamorano
- Endocrinology and Nutrition Department, Complejo Hospitalario Universitario Insular Materno Infantil, Las Palmas de Gran Canaria, Spain
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Maria Castro-Fernandez
- Research Institute for Applied Microelectronics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Maria Del Pino Alberiche Ruano
- Endocrinology and Nutrition Department, Complejo Hospitalario Universitario Insular Materno Infantil, Las Palmas de Gran Canaria, Spain
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Terje Solvoll
- Norwegian Centre for E-health Research, University Hospital of North-Norway, Tromsø, Norway
- Faculty of Nursing and Health Sciences, Nord University, Bodø, Norway
| | - Conceição Granja
- Norwegian Centre for E-health Research, University Hospital of North-Norway, Tromsø, Norway
- Faculty of Nursing and Health Sciences, Nord University, Bodø, Norway
| | - Thomas Roger Schopf
- Norwegian Centre for E-health Research, University Hospital of North-Norway, Tromsø, Norway
| | - Gustavo M Callico
- Research Institute for Applied Microelectronics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - Cristina Soguero-Ruiz
- Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Ana M Wägner
- Endocrinology and Nutrition Department, Complejo Hospitalario Universitario Insular Materno Infantil, Las Palmas de Gran Canaria, Spain
- Instituto Universitario de Investigaciones Biomédicas y Sanitarias, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
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Namburu A, Mohan S, Chakkaravarthy S, Selvaraj P. Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets. SN COMPUTER SCIENCE 2023; 4:228. [DOI: 10.1007/s42979-023-01701-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 01/20/2023] [Indexed: 09/15/2023]
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Taber P, Armin JS, Orozco G, Del Fiol G, Erdrich J, Kawamoto K, Israni ST. Artificial Intelligence and Cancer Control: Toward Prioritizing Justice, Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support Technologies. Curr Oncol Rep 2023; 25:387-424. [PMID: 36811808 DOI: 10.1007/s11912-023-01376-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 02/24/2023]
Abstract
PURPOSE FOR REVIEW This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field's best evidence. RECENT FINDINGS We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews. Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.
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Affiliation(s)
- Peter Taber
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA.
| | - Julie S Armin
- Department of Family and Community Medicine, University of Arizona College of Medicine, Tucson, AZ, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA
| | - Jennifer Erdrich
- Division of Surgical Oncology, University of Arizona College of Medicine, Tucson, AZ, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah School of Medicine, 421 Wakara Way, Salt Lake City, UT, 84108, USA
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Russo GM, Russo A, Urraro F, Cioce F, Gallo L, Belfiore MP, Sangiovanni A, Napolitano S, Troiani T, Verolino P, Sica A, Brancaccio G, Briatico G, Nardone V, Reginelli A. Management of Non-Melanoma Skin Cancer: Radiologists Challenging and Risk Assessment. Diagnostics (Basel) 2023; 13:diagnostics13040793. [PMID: 36832281 PMCID: PMC9955870 DOI: 10.3390/diagnostics13040793] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
Basal cell carcinoma, squamous cell carcinoma, and Merkel cell carcinoma are the three main types of nonmelanoma skin cancers and their rates of occurrence and mortality have been steadily rising over the past few decades. For radiologists, it is still difficult to treat patients with advanced nonmelanoma skin cancer. Nonmelanoma skin cancer patients would benefit greatly from an improved diagnostic imaging-based risk stratification and staging method that takes into account patient characteristics. The risk is especially elevated among those who previously received systemic treatment or phototherapy. Systemic treatments, including biologic therapies and methotrexate (MTX), are effective in managing immune-mediated diseases; however, they may increase susceptibility to NMSC due to immunosuppression or other factors. Risk stratification and staging tools are crucial in treatment planning and prognostic evaluation. PET/CT appears more sensitive and superior to CT and MRI for nodal and distant metastasis as well as in surveillance after surgery. The patient treatment response improved with advent and utilization of immunotherapy and different immune-specific criteria are established to standardized evaluation criteria of clinical trials but none of them have been utilized routinely with immunotherapy. The advent of immunotherapy has also arisen new critical issues for radiologists, such as atypical response pattern, pseudo-progression, as well as immune-related adverse events that require early identification to optimize and improve patient prognosis and management. It is important for radiologists to have knowledge of the radiologic features site of the tumor, clinical stage, histological subtype, and any high-risk features to assess immunotherapy treatment response and immune-related adverse events.
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Affiliation(s)
- Gaetano Maria Russo
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
- Correspondence:
| | - Anna Russo
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Fabrizio Urraro
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Fabrizio Cioce
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Luigi Gallo
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Angelo Sangiovanni
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Stefania Napolitano
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Teresa Troiani
- Unit of Plastic Surgery, Multidisciplinary Department of Medical Surgical and Dental Specialties, University of Campania “Luigi Vanvitelli”, 80120 Naples, Italy
| | - Pasquale Verolino
- Unit of Plastic Surgery, Multidisciplinary Department of Medical Surgical and Dental Specialties, University of Campania “Luigi Vanvitelli”, 80120 Naples, Italy
| | - Antonello Sica
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Gabriella Brancaccio
- Unit of Dermatology, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Giulia Briatico
- Unit of Dermatology, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Valerio Nardone
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80123 Naples, Italy
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Kränke T, Tripolt-Droschl K, Röd L, Hofmann-Wellenhof R, Koppitz M, Tripolt M. New AI-algorithms on smartphones to detect skin cancer in a clinical setting-A validation study. PLoS One 2023; 18:e0280670. [PMID: 36791068 PMCID: PMC9931135 DOI: 10.1371/journal.pone.0280670] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 01/05/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The incidence of skin cancer is rising worldwide and there is medical need to optimize its early detection. This study was conducted to determine the diagnostic and risk-assessment accuracy of two new diagnosis-based neural networks (analyze and detect), which comply with the CE-criteria, in evaluating the malignant potential of various skin lesions on a smartphone. Of note, the intention of our study was to evaluate the performance of these medical products in a clinical setting for the first time. METHODS This was a prospective, single-center clinical study at one tertiary referral center in Graz, Austria. Patients, who were either scheduled for preventive skin examination or removal of at least one skin lesion were eligible for participation. Patients were assessed by at least two dermatologists and by the integrated algorithms on different mobile phones. The lesions to be recorded were randomly selected by the dermatologists. The diagnosis of the algorithm was stated as correct if it matched the diagnosis of the two dermatologists or the histology (if available). The histology was the reference standard, however, if both clinicians considered a lesion as being benign no histology was performed and the dermatologists were stated as reference standard. RESULTS A total of 238 patients with 1171 lesions (86 female; 36.13%) with an average age of 66.19 (SD = 17.05) was included. Sensitivity and specificity of the detect algorithm were 96.4% (CI 93.94-98.85) and 94.85% (CI 92.46-97.23); for the analyze algorithm a sensitivity of 95.35% (CI 93.45-97.25) and a specificity of 90.32% (CI 88.1-92.54) were achieved. DISCUSSION The studied neural networks succeeded analyzing the risk of skin lesions with a high diagnostic accuracy showing that they are sufficient tools in calculating the probability of a skin lesion being malignant. In conjunction with the wide spread use of smartphones this new AI approach opens the opportunity for a higher early detection rate of skin cancer with consecutive lower epidemiological burden of metastatic cancer and reducing health care costs. This neural network moreover facilitates the empowerment of patients, especially in regions with a low density of medical doctors. REGISTRATION Approved and registered at the ethics committee of the Medical University of Graz, Austria (Approval number: 30-199 ex 17/18).
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Affiliation(s)
- Teresa Kränke
- Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria
- * E-mail:
| | | | - Lukas Röd
- Medical University of Graz, Graz, Austria
| | | | | | - Michael Tripolt
- Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria
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Zhou Y, Koyuncu C, Lu C, Grobholz R, Katz I, Madabhushi A, Janowczyk A. Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer. Med Image Anal 2023; 84:102702. [PMID: 36516556 PMCID: PMC9825103 DOI: 10.1016/j.media.2022.102702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022]
Abstract
Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often markedly deteriorates on whole slide images (WSI) generated at external test sites. This phenomenon is due in part to domain shift, wherein differences in test-site pre-analytical variables (e.g., slide scanner, staining procedure) result in WSI with notably different visual presentations compared to training data. To ameliorate pre-analytic variances, approaches such as CycleGAN can be used to calibrate visual properties of images between sites, with the intent of improving DL classifier generalizability. In this work, we present a new approach termed Multi-Site Cross-Organ Calibration based Deep Learning (MuSClD) that employs WSIs of an off-target organ for calibration created at the same site as the on-target organ, based off the assumption that cross-organ slides are subjected to a common set of pre-analytical sources of variance. We demonstrate that by using an off-target organ from the test site to calibrate training data, the domain shift between training and testing data can be mitigated. Importantly, this strategy uniquely guards against potential data leakage introduced during calibration, wherein information only available in the testing data is imparted on the training data. We evaluate MuSClD in the context of the automated diagnosis of non-melanoma skin cancer (NMSC). Specifically, we evaluated MuSClD for identifying and distinguishing (a) basal cell carcinoma (BCC), (b) in-situ squamous cell carcinomas (SCC-In Situ), and (c) invasive squamous cell carcinomas (SCC-Invasive), using an Australian (training, n = 85) and a Swiss (held-out testing, n = 352) cohort. Our experiments reveal that MuSCID reduces the Wasserstein distances between sites in terms of color, contrast, and brightness metrics, without imparting noticeable artifacts to training data. The NMSC-subtyping performance is statistically improved as a result of MuSCID in terms of one-vs. rest AUC: BCC (0.92 vs 0.87, p = 0.01), SCC-In Situ (0.87 vs 0.73, p = 0.15) and SCC-Invasive (0.92 vs 0.82, p = 1e-5). Compared to baseline NMSC-subtyping with no calibration, the internal validation results of MuSClD (BCC (0.98), SCC-In Situ (0.92), and SCC-Invasive (0.97)) suggest that while domain shift indeed degrades classification performance, our on-target calibration using off-target tissue can safely compensate for pre-analytical variabilities, while improving the robustness of the model.
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Affiliation(s)
- Yufei Zhou
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Can Koyuncu
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, USA
| | - Cheng Lu
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Rainer Grobholz
- Institute of Pathology, Cantonal Hospital Aarau, Aarau, Switzerland,Medical Faculty University of Zurich, Zurich, Switzerland
| | - Ian Katz
- Southern Sun Pathology, Sydney, NSW, Australia,University of Queensland, Brisbane, Qld, Australia
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA; Atlanta VA Medical Center, Atlanta, USA.
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA,Department of Oncology, Lausanne University Hospital,Department of Diagnostics, Division of Clinical Pathology, Geneva University Hospitals
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Jartarkar SR, Cockerell CJ, Patil A, Kassir M, Babaei M, Weidenthaler‐Barth B, Grabbe S, Goldust M. Artificial intelligence in Dermatopathology. J Cosmet Dermatol 2022; 22:1163-1167. [PMID: 36548174 DOI: 10.1111/jocd.15565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/14/2022] [Accepted: 12/01/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Ever evolving research in medical field has reached an exciting stage with advent of newer technologies. With the introduction of digital microscopy, pathology has transitioned to become more digitally oriented speciality. The potential of artificial intelligence (AI) in dermatopathology is to aid the diagnosis, and it requires dermatopathologists' guidance for efficient functioning of artificial intelligence. METHOD Comprehensive literature search was performed using electronic online databases "PubMed" and "Google Scholar." Articles published in English language were considered for the review. RESULTS Convolutional neural network, a type of deep neural network, is considered as an ideal tool in image recognition, processing, classification, and segmentation. Implementation of AI in tumor pathology is involved in the diagnosis, grading, staging, and prognostic prediction as well as in identification of genetic or pathological features. In this review, we attempt to discuss the use of AI in dermatopathology, the attitude of patients and clinicians, its challenges, limitation, and potential opportunities in future implementation.
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Affiliation(s)
- Shishira R. Jartarkar
- Department of Dermatology Vydehi Institute of Medical Sciences and Research Centre University‐RGUHS Bengaluru India
| | - Clay J. Cockerell
- Departments of Dermatology and Pathology The University of Texas Southwestern Medical Center Dallas Texas USA
| | - Anant Patil
- Department of Pharmacology Dr. DY Patil Medical College Navi Mumbai India
| | | | - Mahsa Babaei
- School of Medicine Stanford University California USA
| | - Beate Weidenthaler‐Barth
- Department of Dermatology University Medical Center of the Johannes Gutenberg University Mainz Germany
| | - 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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12
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Jartarkar SR. Artificial intelligence: Its role in dermatopathology. Indian J Dermatol Venereol Leprol 2022:1-4. [PMID: 36688886 DOI: 10.25259/ijdvl_725_2021] [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: 07/01/2021] [Accepted: 08/01/2022] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI), a major frontier in the field of medical research, can potentially lead to a paradigm shift in clinical practice. A type of artificial intelligence system known as convolutional neural network points to the possible utility of deep learning in dermatopathology. Though pathology has been traditionally restricted to microscopes and glass slides, recent advancement in digital pathological imaging has led to a transition making it a potential branch for the implementation of artificial intelligence. The current application of artificial intelligence in dermatopathology is to complement the diagnosis and requires a well-trained dermatopathologist's guidance for better designing and development of deep learning algorithms. Here we review the recent advances of artificial intelligence in dermatopathology, its applications in disease diagnosis and in research, along with its limitations and future potential.
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Affiliation(s)
- Shishira R Jartarkar
- Department of Dermatology, Venereology and Leprosy, Vydehi Institute of Medical Sciences and Research Centre, Whitefield, Bengaluru, Karnataka, India
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13
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Tobias MAS, Nogueira BP, Santana MCS, Pires RG, Papa JP, Santos PSS. Artificial intelligence for oral cancer diagnosis: What are the possibilities? Oral Oncol 2022; 134:106117. [PMID: 36099800 DOI: 10.1016/j.oraloncology.2022.106117] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/27/2022] [Accepted: 09/03/2022] [Indexed: 10/31/2022]
Abstract
Oral cancer could be prevented. The primary strategy is based on prevention. Most patients with oral cancer present to the hospital network with advanced staging and a low chance of cure. This condition may be related to physicians' difficulty of making an early diagnosis. With the advancement of information technology, artificial intelligence (AI) holds great promise in terms of assisting in diagnosis. Few machine learning algorithms have been developed for this purpose to date. In this paper, we will discuss the possibilities for diagnosing oral cancer using AI as a tool, as well as the implications for the population. A set of photographic images of oral lesions has been segmented, indicating not only the area of the lesion but also the class of lesion associated with it. Different neural network architectures were trained with the goal of fine segmentation (pixel by pixel), classification of image crops, and classification of whole images based on the presence or absence of a lesion. The accuracy results are acceptable, opening up possibilities not only for identifying lesions but also for classifying the pathology associated with them.
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Affiliation(s)
- Mattheus A S Tobias
- Department of Stomatology, Bauru Dental School, USP - University of São Paulo, Bauru, SP, Brazil.
| | - Bruna P Nogueira
- Department of Stomatology, Bauru Dental School, USP - University of São Paulo, Bauru, SP, Brazil
| | | | - Rafael G Pires
- Department of Computing, São Paulo State University, Bauru, SP, Brazil
| | - João P Papa
- Department of Computing, São Paulo State University, Bauru, SP, Brazil
| | - Paulo S S Santos
- Department of Stomatology, Bauru Dental School, USP - University of São Paulo, Bauru, SP, Brazil
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Alonso-Belmonte C, Montero-Vilchez T, Arias-Santiago S, Buendía-Eisman A. [Translated article] Current State of Skin Cancer Prevention: A Systematic Review. ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2022.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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15
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Situación actual de la prevención del cáncer de piel: una revisión sistemática. ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:781-791. [DOI: 10.1016/j.ad.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/03/2022] [Accepted: 04/12/2022] [Indexed: 11/20/2022] Open
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16
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
An overview of systematic reviews on the application of AI including 129 studies. AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms. Half of the reviews did not evaluate validation procedures nor reporting guidelines. Risk of bias was only included un a third of the reviews. There is not sufficient evidence to transfer AI to actual healthcare delivery.
Background Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. Conclusion Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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Serrano C, Lazo M, Serrano A, Toledo-Pastrana T, Barros-Tornay R, Acha B. Clinically Inspired Skin Lesion Classification through the Detection of Dermoscopic Criteria for Basal Cell Carcinoma. J Imaging 2022; 8:jimaging8070197. [PMID: 35877641 PMCID: PMC9319034 DOI: 10.3390/jimaging8070197] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Background and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.
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Affiliation(s)
- Carmen Serrano
- Dpto. Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain; (M.L.); (B.A.)
- Correspondence:
| | - Manuel Lazo
- Dpto. Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain; (M.L.); (B.A.)
| | - Amalia Serrano
- Hospital Universitario Virgen Macarena, Calle Dr. Fedriani, 3, 41009 Seville, Spain;
| | - Tomás Toledo-Pastrana
- Hospitales Quironsalud Infanta Luisa y Sagrado Corazón, Calle San Jacinto, 87, 41010 Seville, Spain;
| | | | - Begoña Acha
- Dpto. Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain; (M.L.); (B.A.)
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MESSADI MAHAMMED, MAHMOUDI SA, BESSAID ABDELHAFID. ANALYSIS OF SPECIFIC PARAMETERS FOR SKIN TUMOR CLASSIFICATION. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422500178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of nonmalignant cutaneous diseases. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly and its related mortality rate is increasing more modestly, and inversely proportional to the tumor’s thickness. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. In this work, we are interested in extracting all specific attributes which can be used for computer-aided diagnosis of melanoma. In the first step of the proposed work, we applied the Dull Razor [Lee T et al., Dullrazor: A software approach to hair removal from images, Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada, Vol. 21, No. 6, pp. 533–543, 1997] technique to images to reduce the influence of small structures, hairs, bubbles, light reflection. In the second step, a new fuzzy level set algorithm is proposed in order to facilitate the medical image segmentation task. It is able to directly evolve from the initial segmentation proposed that uses a spatial fuzzy clustering approach. The controlling parameters of the level set evolution are also estimated from the results of the fuzzy clustering step. This step is essential to characterize the shape of the lesion and also to locate the tumor to be analyzed. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics as color, texture or form. The method used in this paper is called ABCD. It requires calculating four factors: Asymmetry ([Formula: see text], Border ([Formula: see text], Color ([Formula: see text], and Diversity ([Formula: see text]. Finally, these parameters are used to construct a classification module based on artificial neural network for the recognition of malignant melanoma.
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Affiliation(s)
- MAHAMMED MESSADI
- Biomedical Engineering Laboratory, Department of Electrical and Electronics, Technology Faculty, Abou Bekr Belkaid, Tlemcen University 13000, Laboratory of Biomedical Engineering, Faculty of Technology, University of Tlemcen 13000, Algeria
| | - SAïD MAHMOUDI
- Computer Science Department, Faculty of Engineering, rue de Houdain 9, Mons B-7000, Belgium
| | - ABDELHAFID BESSAID
- Biomedical Engineering Laboratory, Department of Electrical and Electronics, Technology Faculty, Abou Bekr Belkaid, Tlemcen University 13000, Laboratory of Biomedical Engineering, Faculty of Technology, University of Tlemcen 13000, Algeria
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Yoshizawa K, Ando H, Kimura Y, Kawashiri S, Yokomichi H, Moroi A, Ueki K. Automatic discrimination of Yamamoto-Kohama classification by machine learning approach for invasive pattern of oral squamous cell carcinoma using digital microscopic images: a retrospective study. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 133:441-452. [PMID: 35165068 DOI: 10.1016/j.oooo.2021.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/02/2021] [Accepted: 10/06/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE The Yamamoto-Kohama criteria are clinically useful for determining the mode of tumor invasion, especially in Japan. However, this evaluation method is based on subjective visual findings and has led to significant differences in determinations between evaluators and facilities. In this retrospective study, we aimed to develop an automatic method of determining the mode of invasion based on the processing of digital medical images. STUDY DESIGN Using 101 digitized photographic images of anonymized stained specimen slides, we created a classifier that allowed clinicians to introduce feature values and subjected the cases to machine learning using a random forest approach. We then compared the Yamamoto-Kohama grades (1, 2, 3, 4C, 4D) determined by a human oral and maxillofacial surgeon with those determined using the machine learning approach. RESULTS The input of multiple test images into the newly created classifier yielded an overall F-measure value of 87% (grade 1, 93%; grade 2, 67%; grade 3, 89%; grade 4C, 83%; grade 4D, 94%). These results suggest that the output of the classifier was very similar to the judgments of the clinician. CONCLUSIONS This system may be valuable for diagnostic support to provide an accurate determination of the mode of invasion.
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Affiliation(s)
- Kunio Yoshizawa
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan.
| | - Hidetoshi Ando
- Department of Media Engineering, Graduate School of University of Yamanashi, Kofu, Yamanashi, Japan
| | - Yujiro Kimura
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Shuichi Kawashiri
- Department of Oral and Maxillofacial Surgery, Kanazawa University Graduate School of Medical Science, Kanazawa, Ishikawa, Japan
| | - Hiroshi Yokomichi
- Department of Health Sciences, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Akinori Moroi
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Koichiro Ueki
- Department of Oral Maxillofacial Surgery, Division of Medicine, Interdisciplinary Graduate School, University of Yamanashi, Chuo, Yamanashi, Japan
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Skin lesion classification system using a K-nearest neighbor algorithm. Vis Comput Ind Biomed Art 2022; 5:7. [PMID: 35229199 PMCID: PMC8885942 DOI: 10.1186/s42492-022-00103-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/23/2022] [Indexed: 11/10/2022] Open
Abstract
One of the most critical steps in medical health is the proper diagnosis of the disease. Dermatology is one of the most volatile and challenging fields in terms of diagnosis. Dermatologists often require further testing, review of the patient's history, and other data to ensure a proper diagnosis. Therefore, finding a method that can guarantee a proper trusted diagnosis quickly is essential. Several approaches have been developed over the years to facilitate the diagnosis based on machine learning. However, the developed systems lack certain properties, such as high accuracy. This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign. The classification process is effectuated by implementing the K-nearest neighbor (KNN) approach to differentiate between normal skin and malignant skin lesions that imply pathology. KNN is used because it is time efficient and promises highly accurate results. The accuracy of the system reached 98% in classifying skin lesions.
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21
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Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks? APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042092] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised ones.
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22
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Stiff KM, Franklin MJ, Zhou Y, Madabhushi A, Knackstedt TJ. Artificial Intelligence and Melanoma: A Comprehensive Review of Clinical, Dermoscopic, and Histologic Applications. Pigment Cell Melanoma Res 2022; 35:203-211. [PMID: 35038383 DOI: 10.1111/pcmr.13027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/24/2021] [Accepted: 01/09/2022] [Indexed: 11/30/2022]
Abstract
Melanoma detection, prognosis, and treatment represent challenging and complex areas of cutaneous oncology with considerable impact on patient outcomes and healthcare economics. Artificial intelligence (AI) applications in these tasks are rapidly developing. Neural networks with increasing levels of sophistication are being implemented in clinical image, dermoscopic image, and histopathologic specimen classification of pigmented lesions. These efforts hold promise of earlier and highly accurate melanoma detection, as well as reliable prognostication and prediction of therapeutic response. Herein, we provide a brief introduction to AI, discuss contemporary investigational applications of AI in melanoma, and summarize challenges encountered with AI.
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Affiliation(s)
| | | | - Yufei Zhou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland
| | - Thomas J Knackstedt
- Department of Dermatology, MetroHealth System, Cleveland.,School of Medicine, Case Western Reserve University, Cleveland
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23
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. [Translated article] Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2021.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
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24
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Inteligencia artificial en dermatología: ¿amenaza u oportunidad? ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:30-46. [DOI: 10.1016/j.ad.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/18/2021] [Indexed: 11/25/2022] Open
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25
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Sounderajah V, Normahani P, Aggarwal R, Jayakumar S, Markar SR, Ashrafian H, Darzi A. Reporting Standards and Quality Assessment Tools in Artificial Intelligence–Centered Healthcare Research. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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26
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Calin MA, Parasca SV. Automatic detection of basal cell carcinoma by hyperspectral imaging. JOURNAL OF BIOPHOTONICS 2022; 15:e202100231. [PMID: 34427393 DOI: 10.1002/jbio.202100231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/19/2021] [Indexed: 06/13/2023]
Abstract
The purpose of this study was to test the ability of hyperspectral imaging (HSI) combined with unsupervised anomaly detectors to automatically differentiate basal cell carcinoma (BCC) from normal skin. Hyperspectral images of the face of a female patient with a BCC of the lower lip were acquired using a visible/near-infrared HSI system and two anomaly detection algorithms (Reed-Xiaoli and Reed-Xiaoli/Uniform Target hybrid anomaly detectors) were used to detect pathological tissue from normal skin. The results revealed that the receiver operating characteristic curve of the Reed-Xiaoli/Uniform Target hybrid detector was higher than that of the Reed-Xiaoli detector in the range of false positive rates between 0 and 0.8. The area under curve values were good (0.7074 and 0.8607, respectively) with Reed-Xiaoli/Uniform Target hybrid detector performing better. In conclusion, HSI combined with either of two anomaly detectors can play a promising role in the automated screening of BCC.
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Affiliation(s)
- Mihaela Antonina Calin
- Optoelectronic Methods for Biomedical Applications Department, National Institute of Research and Development for Optoelectronics INOE 2000, Magurele, Ilfov, Romania
| | - Sorin Viorel Parasca
- Plastic and Reconstructive Surgery Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Emergency Clinical Hospital for Plastic, Reconstructive Surgery and Burns, Bucharest, Romania
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27
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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28
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Kumar Y, Gupta S, Singla R, Hu YC. A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 29:2043-2070. [PMID: 34602811 PMCID: PMC8475374 DOI: 10.1007/s11831-021-09648-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 09/11/2021] [Indexed: 05/05/2023]
Abstract
Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology & Engineering, Indus University, Rancharda, Via: Shilaj, Ahmedabad, Gujarat 382115 India
| | - Surbhi Gupta
- School of Computer Science and Engineering, Model Institute of Engineering and Technology, Kot bhalwal, Jammu, J&K 181122 India
| | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, Chandigarh Group of Colleges, Landran, Mohali India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, Taichung City, Taiwan, ROC
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29
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Spiegel JM, Ehrlich R, Yassi A, Riera F, Wilkinson J, Lockhart K, Barker S, Kistnasamy B. Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach. Ann Glob Health 2021; 87:58. [PMID: 34249620 PMCID: PMC8252970 DOI: 10.5334/aogh.3206] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing "disruptive" technologies persists. Intrinsic and contextual factors influencing where and how such innovations are introduced therefore require careful scrutiny to ensure that health equity is promoted. To illustrate one such critical approach, we describe and appraise an AI application - the development of computer assisted diagnosis (CAD) to support more efficient adjudication of compensation claims from former gold miners with occupational lung disease in Southern Africa. In doing so, we apply a bio-ethical lens that considers the principles of beneficence, non-maleficence, autonomy and justice and add explicability as a core principle. We draw on the AI literature, our research on CAD validation and process efficiency, as well as apprehensions of users and stakeholders. Issues of concern included AI accuracy, biased training of AI systems, data privacy, impact on human skill development, transparency and accountability in AI use, as well as intellectual property ownership. We discuss ways in which each of these potential obstacles to successful use of CAD could be mitigated. We conclude that efforts to overcoming technical challenges in applying AI must be accompanied from the onset by attention to ensuring its ethical use.
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Affiliation(s)
- Jerry M. Spiegel
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada
| | - Rodney Ehrlich
- School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Annalee Yassi
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada
| | | | | | - Karen Lockhart
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada
| | - Stephen Barker
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada
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30
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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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31
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Lee EY, Maloney NJ, Cheng K, Bach DQ. Machine learning for precision dermatology: Advances, opportunities, and outlook. J Am Acad Dermatol 2021; 84:1458-1459. [PMID: 32645400 PMCID: PMC8023050 DOI: 10.1016/j.jaad.2020.06.1019] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 06/08/2020] [Accepted: 06/26/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Ernest Y Lee
- Department of Bioengineering, University of California-Los Angeles; Division of Dermatology, Department of Medicine, University of California-Los Angeles; University of California-Los Angeles-Caltech Medical Scientist Training Program, David Geffen School of Medicine at University of California-Los Angeles.
| | - Nolan J Maloney
- Division of Dermatology, Department of Medicine, University of California-Los Angeles
| | - Kyle Cheng
- Division of Dermatology, Department of Medicine, University of California-Los Angeles
| | - Daniel Q Bach
- Division of Dermatology, Department of Medicine, University of California-Los Angeles
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32
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Akkoca Gazioğlu BS, Kamaşak ME. Effects of objects and image quality on melanoma classification using deep neural networks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102530] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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33
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Sounderajah V, Normahani P, Aggarwal R, Jayakumar S, Markar SR, Ashrafian H, Darzi A. Reporting Standards and Quality Assessment Tools in Artificial Intelligence Centered Healthcare Research. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_34-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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34
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Dulmage B, Tegtmeyer K, Zhang MZ, Colavincenzo M, Xu S. A Point-of-Care, Real-Time Artificial Intelligence System to Support Clinician Diagnosis of a Wide Range of Skin Diseases. J Invest Dermatol 2020; 141:1230-1235. [PMID: 33065109 DOI: 10.1016/j.jid.2020.08.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 08/01/2020] [Accepted: 08/16/2020] [Indexed: 11/17/2022]
Abstract
Dermatological diagnosis remains challenging for nonspecialists because the morphologies of primary skin lesions widely vary from patient to patient. Although previous studies have used artificial intelligence (AI) to classify lesions as benign or malignant, there have not been extensive studies examining the use of AI on identifying and categorizing a primary skin lesion's morphology. In this study, we evaluate the performance of a standalone AI tool to correctly categorize a skin lesion's morphology from a test bank of images. To provide a marker of performance, we evaluate the accuracy of primary care physicians to categorize skin lesion morphology in the same test bank of images without any aids and then with the aid of a simple visual guide. The AI system achieved an accuracy of 68% in determining the single most likely morphology from the test image bank. When the AI's top prediction was broadened to its top three most likely predictions, accuracy improved to 80%. In comparison, the diagnostic accuracy of primary care physicians was 36% without any aids and 68% with the visual guide (P < 0.001). The AI was subsequently tested on an additional set of 222 heterogeneous images of varying Fitzpatrick skin types and achieved an overall accuracy of 70% in the Fitzpatrick I-III skin type group and 68% in the Fitzpatrick IV-VI skin type group (P = 0.79). An AI is a powerful tool to assist physicians in the diagnosis of skin lesions while still requiring the user to critically consider other possible diagnoses.
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Affiliation(s)
- Brittany Dulmage
- Department of Dermatology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Kyle Tegtmeyer
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Michael Z Zhang
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Maria Colavincenzo
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Shuai Xu
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; Querrey Institute for Bioelectronics, Northwestern University, Evanston, Illinois, USA.
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Pangti R, Chouhan V, Mathur J, Kumar S, Dixit A, Gupta S, Mahajan S, Gupta A, Gupta S. Performance of a deep learning-based application for the diagnosis of basal cell carcinoma in Indian patients as compared to dermatologists and nondermatologists. Int J Dermatol 2020; 60:e51-e52. [PMID: 33040407 DOI: 10.1111/ijd.15242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 08/18/2020] [Accepted: 09/16/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Rashi Pangti
- Department of Dermatology & Venereology, All India Institute of Medical Sciences, New Delhi, India
| | | | | | | | - Ambika Dixit
- Department of Dermatology & Venereology, All India Institute of Medical Sciences, New Delhi, India
| | - Sanjeev Gupta
- Maharishi Markandeshwar Institute of Medical Sciences and Research, Ambala, India
| | - Soniya Mahajan
- Department of Dermatology & Venereology, All India Institute of Medical Sciences, New Delhi, India
| | - Atula Gupta
- Skin Aid Clinic, Cross Point Mall, Gurugram, India
| | - Somesh Gupta
- Department of Dermatology & Venereology, All India Institute of Medical Sciences, New Delhi, India
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36
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Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175954] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The detection of skin diseases is becoming one of the priority tasks worldwide due to the increasing amount of skin cancer. Computer-aided diagnosis is a helpful tool to help dermatologists in the detection of these kinds of illnesses. This work proposes a computer-aided diagnosis based on 1D fractal signatures of texture-based features combining with deep-learning features using transferred learning based in Densenet-201. This proposal works with three 1D fractal signatures built per color-image. The energy, variance, and entropy of the fractal signatures are used combined with 100 features extracted from Densenet-201 to construct the features vector. Because commonly, the classes in the dataset of skin lesion images are imbalanced, we use the technique of ensemble of classifiers: K-nearest neighbors and two types of support vector machines. The computer-aided diagnosis output was determined based on the linear plurality vote. In this work, we obtained an average accuracy of 97.35%, an average precision of 91.61%, an average sensitivity of 66.45%, and an average specificity of 97.85% in the eight classes’ classification in the International Skin Imaging Collaboration (ISIC) archive-2019.
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Marx J, Bangemann M, Abeck D. [Skin cancer screening in the general practice]. MMW Fortschr Med 2020; 162:46-55. [PMID: 32780414 DOI: 10.1007/s15006-020-0019-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Affiliation(s)
| | | | - Dietrich Abeck
- Hautzentrum Nymphenburg, Renatastr. 72, D-80639, München, Deutschland.
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Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W. Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations. Dermatol Ther (Heidelb) 2020; 10:365-386. [PMID: 32253623 PMCID: PMC7211783 DOI: 10.1007/s13555-020-00372-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.
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Affiliation(s)
- Stephanie Chan
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Vidhatha Reddy
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Bridget Myers
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Quinn Thibodeaux
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Nicholas Brownstone
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Wilson Liao
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA.
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Rehman A, Khan MA, Mehmood Z, Saba T, Sardaraz M, Rashid M. Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction. Microsc Res Tech 2020; 83:410-423. [PMID: 31898863 DOI: 10.1002/jemt.23429] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 11/26/2019] [Accepted: 12/15/2019] [Indexed: 11/06/2022]
Abstract
The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel-based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean-based function is implemented and fed input to top-hat and bottom-hat filters which later fused for contrast stretching, (b) seed region growing and graph-cut method-based lesion segmentation and fused both segmented lesions through pixel-based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy-based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.
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Affiliation(s)
- Amjad Rehman
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | | | - Zahid Mehmood
- Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan
| | - Tanzila Saba
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia
| | - Muhammad Sardaraz
- Department of Computer Science, COMSATS University Islamabad, Attock, Pakistan
| | - Muhammad Rashid
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
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40
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Quoi de neuf en recherche ? Ann Dermatol Venereol 2019; 146:12S19-12S23. [DOI: 10.1016/s0151-9638(20)30102-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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41
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Tran BX, Latkin CA, Sharafeldin N, Nguyen K, Vu GT, Tam WWS, Cheung NM, Nguyen HLT, Ho CSH, Ho RCM. Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis. JMIR Med Inform 2019; 7:e14401. [PMID: 31573929 PMCID: PMC6774235 DOI: 10.2196/14401] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/27/2019] [Accepted: 07/19/2019] [Indexed: 01/08/2023] Open
Abstract
Background Artificial intelligence (AI)–based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner. Objective The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research. Methods An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics. Results From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (3) AI-based prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life, and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches are largely driven by machine learning, artificial neural networks, and AI in various clinical practices. Conclusions The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.
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Affiliation(s)
- Bach Xuan Tran
- Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.,Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Carl A Latkin
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Noha Sharafeldin
- Division of Hematology & Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States.,Institute for Cancer Outcomes and Survivorship, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Katherina Nguyen
- Department of Science, Technology, and Society, Stanford University, Palo Alto, CA, United States
| | - Giang Thu Vu
- Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Wilson W S Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ngai-Man Cheung
- Center of Excellence in Artificial Intelligence in Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam.,Information Systems Technology and Design, Singapore University of Technology and Design, Singapore, Singapore
| | | | - Cyrus S H Ho
- Department of Psychological Medicine, National University Hospital, Singapore, Singapore
| | - Roger C M Ho
- Center of Excellence in Behavior Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam.,Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.,Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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