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Seoni S, Shahini A, Meiburger KM, Marzola F, Rotunno G, Acharya UR, Molinari F, Salvi M. All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108200. [PMID: 38677080 DOI: 10.1016/j.cmpb.2024.108200] [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: 01/27/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Alen Shahini
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Kristen M Meiburger
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Francesco Marzola
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Giulia Rotunno
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Centre for Health Research, University of Southern Queensland, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
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Gordon ER, Trager MH, Kontos D, Weng C, Geskin LJ, Dugdale LS, Samie FH. Ethical considerations for artificial intelligence in dermatology: a scoping review. Br J Dermatol 2024; 190:789-797. [PMID: 38330217 DOI: 10.1093/bjd/ljae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.
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Affiliation(s)
- Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Megan H Trager
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Despina Kontos
- University of Pennsylvania, Perelman School of Medicine, Department of Radiology, Philadelphia, PA, USA
- Radiology
| | | | - Larisa J Geskin
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Lydia S Dugdale
- Columbia University Vagelos College of Physicians and Surgeons, Department of Medicine, Center for Clinical Medical Ethics, New York, NY, USA
| | - Faramarz H Samie
- Columbia University Irving Medical Center, Departments of Dermatology
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Ribeiro RDPES, von Wangenheim A. Automated Image Quality and Protocol Adherence Assessment of Examinations in Teledermatology: First Results. Telemed J E Health 2024; 30:994-1005. [PMID: 37930716 DOI: 10.1089/tmj.2023.0155] [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] [Indexed: 11/07/2023] Open
Abstract
Introduction: Image quality and acquisition protocol adherence assessment is a neglected area in teledermatology. We examine if it is feasible to use deep learning methods to automate the assessment of the adherence of examinations to image acquisition protocols. In this study, we focused on the quality criteria of two image acquisition protocols: (1) approximation image and (2) panoramic image, as these are present in all teledermatology examination protocols currently used by the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC). Methods: We use a data set of 36,102 teledermatological examinations performed at the STT/SC during 2021. As our validation process, we adopted standard machine learning metrics and an inter-rater agreement (IRA) study with 11 dermatologists. For the approximation image protocol, we used the Mask-Region based Convolutional Neural Network (RCNN) Object Detection Deep Learning (DL) architecture to identify the presence of a lesion identification tag and a ruler used to provide a frame reference of the lesion. For the panoramic image protocol, we used DensePose, a pose estimation DL, architecture to assess the presence of a whole patient body and its orientation. A combination of the two approaches was additionally validated through an IRA study between specialists. Results: Mask-RCNN achieved a score of 96% mean average precision (mAP), while DensePose presented 75% mAP. IRA achieved a level of agreement of 96.68% with the Krippendorff alpha score. Conclusions: Our results show the feasibility of using deep learning to automate the image quality and protocol adherence assessment in teledermatology, before the specialist's manual analysis of the examination.
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Affiliation(s)
- Rodrigo de Paula E Silva Ribeiro
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
- Image Processing and Computer Graphics Lab, INCoD, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
| | - Aldo von Wangenheim
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
- Image Processing and Computer Graphics Lab, INCoD, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil
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Reddy S, Giri D, Patel R. Artificial Intelligence-Based Distinction of Actinic Keratosis and Seborrheic Keratosis. Cureus 2024; 16:e58692. [PMID: 38774175 PMCID: PMC11108590 DOI: 10.7759/cureus.58692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2024] [Indexed: 05/24/2024] Open
Abstract
Actinic keratosis (AK) and seborrheic keratosis (SK) represent prevalent dermatological conditions with distinct clinical characteristics and potential health implications. This article investigates recent strides in dermatological diagnostics, centered on the development and application of artificial intelligence (AI) technology for discerning between AK and SK. The objective of this study is to develop and evaluate an artificial intelligence (AI) model capable of accurately distinguishing between stage one and stage two gastric carcinoma based on pathology slides. Employing a dataset of high-resolution images obtained from Kaggle.com, consisting of 1000 AK and 1000 SK images, a novel AI model was trained using cutting-edge deep learning methodologies. The dataset underwent meticulous partitioning into training, validation, and testing subsets to ensure robustness and generalizability. The AI model showcased exceptional proficiency in distinguishing AK from SK images, attaining notable levels of accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC). Insights into the etiology and clinical ramifications of AK and SK were presented, emphasizing the critical significance of precise diagnosis and tailored therapeutic approaches. The integration of AI technology into dermatological practice holds considerable potential for enhancing diagnostic precision, refining treatment decisions, and elevating patient outcomes. This article underscores the transformative impact of AI in dermatology and the importance of collaborative efforts between clinicians, researchers, and technologists in advancing the realm of dermatological diagnosis and care.
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Affiliation(s)
- Shreya Reddy
- Biomedical Sciences, Creighton University, Omaha, USA
| | - Dinesh Giri
- Research, California Northstate University College of Medicine, Elk Grove, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University Quillen College of Medicine, Johnson City, USA
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Deps PD, Yotsu R, Furriel BCRS, de Oliveira BD, de Lima SL, Loureiro RM. The potential role of artificial intelligence in the clinical management of Hansen's disease (leprosy). Front Med (Lausanne) 2024; 11:1338598. [PMID: 38523910 PMCID: PMC10959095 DOI: 10.3389/fmed.2024.1338598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/20/2024] [Indexed: 03/26/2024] Open
Abstract
Missed and delayed diagnoses of Hansen's disease (HD) are making the battle against it even more complex, increasing its transmission and significantly impacting those affected and their families. This strains public health systems and raises the risk of lifelong impairments and disabilities. Worryingly, the three countries most affected by HD witnessed a growth in new cases in 2022, jeopardizing the World Health Organization's targets to interrupt transmission. Artificial intelligence (AI) can help address these challenges by offering the potential for rapid case detection, customized treatment, and solutions for accessibility challenges-especially in regions with a shortage of trained healthcare professionals. This perspective article explores how AI can significantly impact the clinical management of HD, focusing on therapeutic strategies. AI can help classify cases, ensure multidrug therapy compliance, monitor geographical treatment coverage, and detect adverse drug reactions and antimicrobial resistance. In addition, AI can assist in the early detection of nerve damage, which aids in disability prevention and planning rehabilitation. Incorporating AI into mental health counseling is also a promising contribution to combating the stigma associated with HD. By revolutionizing therapeutic approaches, AI offers a holistic solution to reduce the burden of HD and improve patient outcomes.
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Affiliation(s)
- Patrícia D. Deps
- Department of Social Medicine, Health Sciences Center, Federal University of Espirito Santo, Vitoria, Brazil
- Department of Radiology, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Rie Yotsu
- Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | | | | | - Sergio L. de Lima
- Department of Radiology, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Rafael M. Loureiro
- Department of Radiology, Hospital Israelita Albert Einstein, São Paulo, Brazil
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6
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Joly-Chevrier M, Nguyen AXL, Liang L, Lesko-Krleza M, Lefrançois P. The State of Artificial Intelligence in Skin Cancer Publications. J Cutan Med Surg 2024; 28:146-152. [PMID: 38323537 PMCID: PMC11015717 DOI: 10.1177/12034754241229361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting. OBJECTIVES To analyze the characteristics and trends of AI skin cancer publications from dermatology journals. METHODS AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI. RESULTS A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%). CONCLUSIONS Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.
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Affiliation(s)
| | | | - Laurence Liang
- Faculty of Engineering, McGill University, Montreal, QC, Canada
| | - Michael Lesko-Krleza
- Division of Computer Engineering, Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
| | - Philippe Lefrançois
- Division of Dermatology, Department of Medicine, McGill University, Montreal, QC, Canada
- Division of Dermatology, Department of Medicine, Jewish General Hospital, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
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7
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Kontzias C, Pixley JN, Zaino M, Feldman SR. Validation of a new facial skin analysis device across Fitzpatrick skin types. J Cosmet Dermatol 2024; 23:720-721. [PMID: 37792416 DOI: 10.1111/jocd.16024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/25/2023] [Indexed: 10/05/2023]
Affiliation(s)
- Christina Kontzias
- Department of Dermatology, Center for Dermatology Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Jessica N Pixley
- Department of Dermatology, Center for Dermatology Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Mallory Zaino
- Department of Dermatology, Center for Dermatology Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Steven R Feldman
- Department of Dermatology, Center for Dermatology Research, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
- Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
- Department of Social Sciences & Health Policy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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8
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Fernandes JRN, Teles AS, Fernandes TRS, Lima LDB, Balhara S, Gupta N, Teixeira S. Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review. J Clin Med 2023; 13:180. [PMID: 38202187 PMCID: PMC10779723 DOI: 10.3390/jcm13010180] [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: 11/02/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
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Affiliation(s)
- Jacks Renan Neves Fernandes
- PhD Program in Biotechnology—Northeast Biotechnology Network, Federal University of Piauí, Teresina 64049-550, Brazil;
| | - Ariel Soares Teles
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
- Federal Institute of Maranhão, Araioses 65570-000, Brazil
| | - Thayaná Ribeiro Silva Fernandes
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Lucas Daniel Batista Lima
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
| | - Surjeet Balhara
- Department of Electronics & Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India;
| | - Nishu Gupta
- Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway;
| | - Silmar Teixeira
- Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil; (T.R.S.F.); (L.D.B.L.); (S.T.)
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Liu Q, Zhang J, Bai Y. Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis. Front Oncol 2023; 13:1222426. [PMID: 37901316 PMCID: PMC10613074 DOI: 10.3389/fonc.2023.1222426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/18/2023] [Indexed: 10/31/2023] Open
Abstract
Objective Artificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the current knowledge regarding the utilization of AI in skin cancer diagnosis remains somewhat limited, necessitating further research. This study employs visual bibliometric analysis to consolidate and present insights into the evolution and deployment of AI in the context of skin cancer. Through this analysis, we aim to shed light on the research developments, focal areas of interest, and emerging trends within AI and its application to skin cancer diagnosis. Methods On July 14, 2023, articles and reviews about the application of AI in skin cancer, spanning the years from 1900 to 2023, were selected from the Web of Science Core Collection. Co-authorship, co-citation, and co-occurrence analyses of countries, institutions, authors, references, and keywords within this field were conducted using a combination of tools, including CiteSpace V (version 6.2. R3), VOSviewer (version 1.6.18), SCImago, Microsoft Excel 2019, and R 4.2.3. Results A total of 512 papers matching the search terms and inclusion/exclusion criteria were published between 1991 and 2023. The United States leads in publications with 149, followed by India with 61. Germany holds eight positions among the top 10 institutions, while the United States has two. The most prevalent journals cited were Cancer, the European Journal of Cancer, and Sensors. The most frequently cited keywords include "skin cancer", "classification", "artificial intelligence", and "deep learning". Conclusions Research into the application of AI in skin cancer is rapidly expanding, and an increasing number of scholars are dedicating their efforts to this field. With the advancement of AI technology, new opportunities have arisen to enhance the accuracy of skin imaging diagnosis, treatment based on big data, and prognosis prediction. However, at present, the majority of AI research in the field of skin cancer diagnosis is still in the feasibility study stage. It has not yet made significant progress toward practical implementation in clinical settings. To make substantial strides in this field, there is a need to enhance collaboration between countries and institutions. Despite the potential benefits of AI in skin cancer research, numerous challenges remain to be addressed, including developing robust algorithms, resolving data quality issues, and enhancing results interpretability. Consequently, sustained efforts are essential to surmount these obstacles and facilitate the practical application of AI in skin cancer research.
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Affiliation(s)
- Qianwei Liu
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Jie Zhang
- Library, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yanping Bai
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, China
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Marri SS, Inamadar AC, Janagond AB, Albadri W. Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study. JMIR DERMATOLOGY 2023; 6:e45529. [PMID: 37632978 PMCID: PMC10335135 DOI: 10.2196/45529] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary. OBJECTIVE This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist. METHODS This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated diagnoses (prediction accuracy) was used to evaluate the app's effectiveness. Sensitivity, specificity, and positive predictive value were also used to assess the app's performance. Chi-square test was used to contrast categorical variables. P<.05 was considered statistically significant. RESULTS A total of 600 patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumors, and infections. In the anticipated top three diagnoses, the app's mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%), while for the exact diagnosis, it was 80.6% (95% CI 77.2%-83.7%). The prediction accuracy (top one) for alopecia, acne, pigmentary disorders, and fungal infections was 97.7%, 91.7%, 88.5%, and 82.9%, respectively. Prediction accuracy (top three) for alopecia, eczema, and tumors was 100%. The sensitivity and specificity of the app were 97% (95% CI 95%-98%) and 98% (95% CI 98%-99%), respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all conditions (P<.001). CONCLUSIONS The AI app has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability.
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Affiliation(s)
- Shiva Shankar Marri
- Department of Dermatology, Venereology and Leprosy, Shri B M Patil Medical College, Hospital and Research Centre, BLDE (Deemed to be University), Vijayapur, Karnataka, India
| | - Arun C Inamadar
- Department of Dermatology, Venereology and Leprosy, Shri B M Patil Medical College, Hospital and Research Centre, BLDE (Deemed to be University), Vijayapur, Karnataka, India
| | - Ajit B Janagond
- Department of Dermatology, Venereology and Leprosy, Shri B M Patil Medical College, Hospital and Research Centre, BLDE (Deemed to be University), Vijayapur, Karnataka, India
| | - Warood Albadri
- Department of Dermatology, Venereology and Leprosy, Shri B M Patil Medical College, Hospital and Research Centre, BLDE (Deemed to be University), Vijayapur, Karnataka, India
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Vakharia KT. Clinical Diagnosis and Classification: Including Biopsy Techniques and Noninvasive Imaging. Clin Plast Surg 2021; 48:577-585. [PMID: 34503718 DOI: 10.1016/j.cps.2021.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Early detection of melanoma is important in improving patient survival. The treatment of melanoma is multidisciplinary and begins by obtaining an accurate diagnosis. The mainstays of melanoma diagnosis include examination of the lesion and surrounding areas and an excisional biopsy so that a pathologic diagnosis can be obtained. The pathology results will help guide treatment recommendations, and some information can be used for prognosis. Further workup of the patient may include laboratory studies and imaging for staging and surveillance.
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Affiliation(s)
- Kavita T Vakharia
- Department of Plastic Surgery, Cleveland Clinic, 9500 Euclid Avenue, A51, Cleveland, OH 44195, USA.
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