1
|
Krakowski I, Kim J, Cai ZR, Daneshjou R, Lapins J, Eriksson H, Lykou A, Linos E. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:78. [PMID: 38594408 PMCID: PMC11004168 DOI: 10.1038/s41746-024-01031-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/05/2024] [Indexed: 04/11/2024] Open
Abstract
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
Collapse
Affiliation(s)
- Isabelle Krakowski
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Jiyeong Kim
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Zhuo Ran Cai
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA
| | - Roxana Daneshjou
- Department of Dermatology, Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Jan Lapins
- Department of Dermatology, Theme Inflammation, Karolinska University Hospital, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Theme Cancer, Unit of Head-Neck-, Lung- and Skin Cancer, Skin Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Anastasia Lykou
- Department of Education, University of Nicosia, Nicosia, Cyprus
| | - Eleni Linos
- Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Dermatology, Stanford, Stanford University, Stanford, CA, USA.
| |
Collapse
|
2
|
Hadi MU, Qureshi R, Ahmed A, Iftikhar N. A lightweight CORONA-NET for COVID-19 detection in X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 225:120023. [PMID: 37063778 PMCID: PMC10088342 DOI: 10.1016/j.eswa.2023.120023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors.
Collapse
Affiliation(s)
- Muhammad Usman Hadi
- Nanotechnology and Integrated Bio-Engineering Centre (NIBEC), School of Engineering, Ulster University, BT15 1AP Belfast, UK
| | - Rizwan Qureshi
- Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX 77030, USA
| | - Ayesha Ahmed
- Department of Radiology, Aalborg University Hospital, Aalborg 9000, Denmark
| | - Nadeem Iftikhar
- University College of Northern Denmark, Aalborg 9200, Denmark
| |
Collapse
|
3
|
Kim DH, Sun S, Cho SI, Kong HJ, Lee JW, Lee JH, Suh DH. Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists. Am J Clin Dermatol 2023:10.1007/s40257-023-00777-5. [PMID: 37160644 DOI: 10.1007/s40257-023-00777-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. OBJECTIVES We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. METHODS A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. RESULTS In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. CONCLUSIONS Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.
Collapse
Affiliation(s)
- Dong Hyo Kim
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea
| | - Sukkyu Sun
- Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea
| | - Soo Ick Cho
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyoun-Joong Kong
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Ji Won Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jun Hyo Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea
| | - Dae Hun Suh
- Department of Dermatology, Seoul National University College of Medicine, Seoul, South Korea.
- Acne, Rosacea, Seborrheic Dermatitis and Hidradenitis Suppurativa Research Laboratory, Seoul National University Hospital, Seoul, South Korea.
| |
Collapse
|
4
|
Differentiating malignant and benign eyelid lesions using deep learning. Sci Rep 2023; 13:4103. [PMID: 36914694 PMCID: PMC10011394 DOI: 10.1038/s41598-023-30699-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0-89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8-90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations. JID INNOVATIONS 2022; 3:100150. [PMID: 36655135 PMCID: PMC9841357 DOI: 10.1016/j.xjidi.2022.100150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/17/2022] [Accepted: 07/15/2022] [Indexed: 01/21/2023] Open
Abstract
Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks. Furthermore, the review summarizes the current landscape of image datasets, transfer learning approaches, challenges, and limitations within current AI literature and current regulatory pathways for approval of models as clinical decision support tools.
Collapse
|
7
|
Ono S, Goto T. Introduction to supervised machine learning in clinical epidemiology. ANNALS OF CLINICAL EPIDEMIOLOGY 2022; 4:63-71. [PMID: 38504945 PMCID: PMC10760492 DOI: 10.37737/ace.22009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Machine learning refers to a series of processes in which a computer finds rules from a vast amount of data. With recent advances in computer technology and the availability of a wide variety of health data, machine learning has rapidly developed and been applied in medical research. Currently, there are three types of machine learning: supervised, unsupervised, and reinforcement learning. In medical research, supervised learning is commonly used for diagnoses and prognoses, while unsupervised learning is used for phenotyping a disease, and reinforcement learning for maximizing favorable results, such as optimization of total patients' waiting time in the emergency department. The present article focuses on the concept and application of supervised learning in medicine, the most commonly used machine learning approach in medicine, and provides a brief explanation of four algorithms widely used for prediction (random forests, gradient-boosted decision tree, support vector machine, and neural network). Among these algorithms, the neural network has further developed into deep learning algorithms to solve more complex tasks. Along with simple classification problems, deep learning is commonly used to process medical imaging, such as retinal fundus photographs for diabetic retinopathy diagnosis. Although machine learning can bring new insights into medicine by processing a vast amount of data that are often beyond human capacity, algorithms can also fail when domain knowledge is neglected. The combination of algorithms and human cognitive ability is a key to the successful application of machine learning in medicine.
Collapse
Affiliation(s)
- Sachiko Ono
- Department of Eat-loss Medicine, Graduate School of Medicine, The University of Tokyo
| | - Tadahiro Goto
- Department of Clinical Epidemiology and Health Economics, The University of Tokyo
- TXP Medical Co. Ltd
| |
Collapse
|
8
|
Willem T, Krammer S, Böhm A, French LE, Hartmann D, Lasser T, Buyx A. Risks and benefits of dermatological machine learning healthcare applications – an overview and ethical analysis. J Eur Acad Dermatol Venereol 2022; 36:1660-1668. [DOI: 10.1111/jdv.18192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 04/07/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Theresa Willem
- Technical University of Munich School of Medicine, Institute of History and Ethics in Medicine Germany
- Technical University of Munich School of Social Sciences and Technology, Department of Science, Technology and Society (STS)
| | - Sebastian Krammer
- Ludwig Maximilian University of Munich Department of Dermatology and Allergology Munich Germany
| | - Anne‐Sophie Böhm
- Ludwig Maximilian University of Munich Department of Dermatology and Allergology Munich Germany
| | - Lars E. French
- Ludwig Maximilian University of Munich Department of Dermatology and Allergology Munich Germany
- Dr. Philip Frost Department of Dermatology and Cutaneous Surgery University of Miami Miller School of Medicine Miami FL USA
| | - Daniela Hartmann
- Ludwig Maximilian University of Munich Department of Dermatology and Allergology Munich Germany
| | - Tobias Lasser
- Technical University of Munich School of Computation, Information and Technology, Department of Informatics Germany
- Technical University of Munich Institute of Biomedical Engineering Germany Munich
| | - Alena Buyx
- Technical University of Munich School of Medicine, Institute of History and Ethics in Medicine Germany
| |
Collapse
|
9
|
Shah PM, Ullah F, Shah D, Gani A, Maple C, Wang Y, Abrar M, Islam SU. Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:35094-35105. [PMID: 35582498 PMCID: PMC9088790 DOI: 10.1109/access.2021.3077592] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 04/20/2021] [Indexed: 05/03/2023]
Abstract
In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.
Collapse
Affiliation(s)
- Pir Masoom Shah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
- School of Computer ScienceWuhan University Wuhan 430072 China
| | - Faizan Ullah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
| | - Dilawar Shah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
| | - Abdullah Gani
- Faculty of Computer Science and Information TechnologyUniversity of Malaya Kuala Lumpur 50603 Malaysia
- Faculty of Computing and InformaticsUniversity Malaysia Sabah Labuan 88400 Malaysia
| | - Carsten Maple
- Secure Cyber Systems Research Group, WMGUniversity of Warwick Coventry CV4 7AL U.K
- Alan Turing Institute London NW1 2DB U.K
| | - Yulin Wang
- School of Computer ScienceWuhan University Wuhan 430072 China
| | - Mohammad Abrar
- Department of Computer ScienceMohi-ud-Din Islamic University Nerian Sharif 12080 Pakistan
| | - Saif Ul Islam
- Department of Computer ScienceInstitute of Space Technology Islamabad 44000 Pakistan
| |
Collapse
|
10
|
Zhang L, Mishra S, Zhang T, Zhang Y, Zhang D, Lv Y, Lv M, Guan N, Hu XS, Chen DZ, Han X. Design and Assessment of Convolutional Neural Network Based Methods for Vitiligo Diagnosis. Front Med (Lausanne) 2021; 8:754202. [PMID: 34733869 PMCID: PMC8558218 DOI: 10.3389/fmed.2021.754202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/23/2021] [Indexed: 01/31/2023] Open
Abstract
Background: Today's machine-learning based dermatologic research has largely focused on pigmented/non-pigmented lesions concerning skin cancers. However, studies on machine-learning-aided diagnosis of depigmented non-melanocytic lesions, which are more difficult to diagnose by unaided eye, are very few. Objective: We aim to assess the performance of deep learning methods for diagnosing vitiligo by deploying Convolutional Neural Networks (CNNs) and comparing their diagnosis accuracy with that of human raters with different levels of experience. Methods: A Chinese in-house dataset (2,876 images) and a world-wide public dataset (1,341 images) containing vitiligo and other depigmented/hypopigmented lesions were constructed. Three CNN models were trained on close-up images in both datasets. The results by the CNNs were compared with those by 14 human raters from four groups: expert raters (>10 years of experience), intermediate raters (5-10 years), dermatology residents, and general practitioners. F1 score, the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity metrics were used to compare the performance of the CNNs with that of the raters. Results: For the in-house dataset, CNNs achieved a comparable F1 score (mean [standard deviation]) with expert raters (0.8864 [0.005] vs. 0.8933 [0.044]) and outperformed intermediate raters (0.7603 [0.029]), dermatology residents (0.6161 [0.068]) and general practitioners (0.4964 [0.139]). For the public dataset, CNNs achieved a higher F1 score (0.9684 [0.005]) compared to the diagnosis of expert raters (0.9221 [0.031]). Conclusion: Properly designed and trained CNNs are able to diagnose vitiligo without the aid of Wood's lamp images and outperform human raters in an experimental setting.
Collapse
Affiliation(s)
- Li Zhang
- Department of Dermatology, Qingdao Women and Children's Hospital of Qingdao University, Qingdao, China
| | - Suraj Mishra
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Tianyu Zhang
- Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR China
| | - Yue Zhang
- Department of Dermatology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Duo Zhang
- Department of Dermatology, Affiliated Central Hospital, Shenyang Medical College, Shenyang, China
| | - Yalin Lv
- Department of Dermatology, Affiliated Hospital of Medical College, Qingdao University, Qingdao, China
| | - Mingsong Lv
- Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, SAR China
| | - Nan Guan
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, SAR China
| | - Xiaobo Sharon Hu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Danny Ziyi Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Xiuping Han
- Department of Dermatology, Shengjing Hospital of China Medical University, Shenyang, China
| |
Collapse
|
11
|
Han SS, Moon IJ, Kim SH, Na JI, Kim MS, Park GH, Park I, Kim K, Lim W, Lee JH, Chang SE. Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study. PLoS Med 2020; 17:e1003381. [PMID: 33237903 PMCID: PMC7688128 DOI: 10.1371/journal.pmed.1003381] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 10/19/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings. METHODS AND FINDINGS To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation [SD], 52.1 [18.3]; 4,701 men [45.1%]) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. In addition, the results obtained by the algorithm for the data of randomly selected batches of 30 patients were compared with those obtained by 44 dermatologists in experimental settings; the dermatologists were only provided with multiple images of each lesion, without clinical information. With regard to the determination of malignancy, the area under the curve (AUC) achieved by the algorithm was 0.863 (95% confidence interval [CI] 0.852-0.875), when unprocessed clinical photographs were used. The sensitivity and specificity of the algorithm at the predefined high-specificity threshold were 62.7% (95% CI 59.9-65.1) and 90.0% (95% CI 89.4-90.6), respectively. Furthermore, the sensitivity and specificity of the first clinical impression of 65 attending physicians were 70.2% and 95.6%, respectively, which were superior to those of the algorithm (McNemar test; p < 0.0001). The positive and negative predictive values of the algorithm were 45.4% (CI 43.7-47.3) and 94.8% (CI 94.4-95.2), respectively, whereas those of the first clinical impression were 68.1% and 96.0%, respectively. In the reader test conducted using images corresponding to batches of 30 patients, the sensitivity and specificity of the algorithm at the predefined threshold were 66.9% (95% CI 57.7-76.0) and 87.4% (95% CI 82.5-92.2), respectively. Furthermore, the sensitivity and specificity derived from the first impression of 44 of the participants were 65.8% (95% CI 55.7-75.9) and 85.7% (95% CI 82.4-88.9), respectively, which are values comparable with those of the algorithm (Wilcoxon signed-rank test; p = 0.607 and 0.097). Limitations of this study include the exclusive use of high-quality clinical photographs taken in hospitals and the lack of ethnic diversity in the study population. CONCLUSIONS Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information.
Collapse
Affiliation(s)
- Seung Seog Han
- Department of Dermatology, I Dermatology Clinic, Seoul, Korea
| | - Ik Jun Moon
- Department of Dermatology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seong Hwan Kim
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Jung-Im Na
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Myoung Shin Kim
- Department of Dermatology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea
| | - Gyeong Hun Park
- Department of Dermatology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Korea
| | - Keewon Kim
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Korea
| | | | - Ju Hee Lee
- Department of Dermatology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| |
Collapse
|
12
|
Cho SI, Han B, Hur K, Mun JH. Perceptions and attitudes of medical students regarding artificial intelligence in dermatology. J Eur Acad Dermatol Venereol 2020; 35:e72-e73. [PMID: 32852856 DOI: 10.1111/jdv.16812] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/24/2020] [Accepted: 06/30/2020] [Indexed: 12/21/2022]
Affiliation(s)
- S I Cho
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - B Han
- Seoul National University College of Medicine, Seoul, Korea
| | - K Hur
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - J-H Mun
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea.,Institute of Human-Environment Interface Biology, Seoul National University, Seoul, Korea
| |
Collapse
|
13
|
Kriegsmann M, Haag C, Weis CA, Steinbuss G, Warth A, Zgorzelski C, Muley T, Winter H, Eichhorn ME, Eichhorn F, Kriegsmann J, Christopolous P, Thomas M, Witzens-Harig M, Sinn P, von Winterfeld M, Heussel CP, Herth FJF, Klauschen F, Stenzinger A, Kriegsmann K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers (Basel) 2020; 12:cancers12061604. [PMID: 32560475 PMCID: PMC7352768 DOI: 10.3390/cancers12061604] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 06/14/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022] Open
Abstract
Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.
Collapse
Affiliation(s)
- Mark Kriegsmann
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Correspondence: (M.K.); (K.K.); Tel.: +49-6221-56-36930 (M.K.); +49-6221-56-37238 (K.K.)
| | - Christian Haag
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
| | - Cleo-Aron Weis
- Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, 68782 Mannheim, Germany;
| | - Georg Steinbuss
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
| | - Arne Warth
- Institute of Pathology, Cytopathology, and Molecular Pathology, UEGP MVZ Gießen/Wetzlar/Limburg, 65549 Limburg, Germany;
| | - Christiane Zgorzelski
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
| | - Thomas Muley
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Hauke Winter
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Martin E. Eichhorn
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Florian Eichhorn
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Surgery, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Joerg Kriegsmann
- Molecular Pathology Trier, 54296 Trier, Germany;
- Danube Private University Krems, 3500 Krems, Austria
| | - Petros Christopolous
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Oncology, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | - Michael Thomas
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Thoracic Oncology, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | | | - Peter Sinn
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
| | - Moritz von Winterfeld
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
| | - Claus Peter Heussel
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, Heidelberg University, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Thoraxklinik, Heidelberg University, 69120 Heidelberg, Germany
| | - Felix J. F. Herth
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
- Department of Pneumology and Critical Care Medicine, Thoraxklinik, Heidelberg University, 69126 Heidelberg, Germany
| | | | - Albrecht Stenzinger
- Institute of Pathology, Heidelberg University, 69120 Heidelberg, Germany; (C.H.); (G.S.); (C.Z.); (P.S.); (M.v.W.); (A.S.)
- Translational Lung Research Centre Heidelberg, Member of the German Centre for Lung Research (DZL), 69120 Heidelberg, Germany; (T.M.); (H.W.); (M.E.E.); (F.E.); (P.C.); (M.T.); (C.P.H.); (F.J.F.H.)
| | - Katharina Kriegsmann
- Department Hematology, Oncology and Rheumatology, Heidelberg University, 69120 Heidelberg, Germany
- Correspondence: (M.K.); (K.K.); Tel.: +49-6221-56-36930 (M.K.); +49-6221-56-37238 (K.K.)
| |
Collapse
|
14
|
Kim YJ, Han SS, Yang HJ, Chang SE. Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis. PLoS One 2020; 15:e0234334. [PMID: 32525908 PMCID: PMC7289382 DOI: 10.1371/journal.pone.0234334] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 05/22/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Onychomycosis is the most common nail disorder and is associated with diagnostic challenges. Emerging non-invasive, real-time techniques such as dermoscopy and deep convolutional neural networks have been proposed for the diagnosis of this condition. However, comparative studies of the two tools in the diagnosis of onychomycosis have not previously been conducted. OBJECTIVES This study evaluated the diagnostic abilities of a deep neural network (http://nail.modelderm.com) and dermoscopic examination in patients with onychomycosis. METHODS A prospective observational study was performed in patients presenting with dystrophic features in the toenails. Clinical photographs were taken by research assistants, and the ground truth was determined either by direct microscopy using the potassium hydroxide test or by fungal culture. Five board-certified dermatologists determined a diagnosis of onychomycosis using the clinical photographs. The diagnosis was also made using the algorithm and dermoscopic examination. RESULTS A total of 90 patients (mean age, 55.3; male, 43.3%) assessed between September 2018 and July 2019 were included in the analysis. The detection of onychomycosis using the algorithm (AUC, 0.751; 95% CI, 0.646-0.856) and that by dermoscopy (AUC, 0.755; 95% CI, 0.654-0.855) were seen to be comparable (Delong's test; P = 0.952). The sensitivity and specificity of the algorithm at the operating point were 70.2% and 72.7%, respectively. The sensitivity and specificity of diagnosis by the five dermatologists were 73.0% and 49.7%, respectively. The Youden index of the algorithm (0.429) was also comparable to that of the dermatologists' diagnosis (0.230±0.176; Wilcoxon rank-sum test; P = 0.667). CONCLUSIONS As a standalone method, the algorithm analyzed photographs taken by non-physician and showed comparable accuracy for the diagnosis of onychomycosis to that made by experienced dermatologists and by dermoscopic examination. Large sample size and world-wide, multicentered studies should be investigated to prove the performance of the algorithm.
Collapse
Affiliation(s)
- Young Jae Kim
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Seog Han
- Department of Dermatology, I Dermatology Clinic, Seoul, Korea
- * E-mail: (SEC); (SSH)
| | - Hee Joo Yang
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- * E-mail: (SEC); (SSH)
| |
Collapse
|
15
|
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: 68] [Impact Index Per Article: 17.0] [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.
Collapse
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.
| |
Collapse
|
16
|
Lee S, Chu YS, Yoo SK, Choi S, Choe SJ, Koh SB, Chung KY, Xing L, Oh B, Yang S. Augmented decision-making for acral lentiginous melanoma detection using deep convolutional neural networks. J Eur Acad Dermatol Venereol 2020; 34:1842-1850. [PMID: 31919901 DOI: 10.1111/jdv.16185] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 12/13/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Several studies have achieved high-level performance of melanoma detection using convolutional neural networks (CNNs). However, few have described the extent to which the implementation of CNNs improves the diagnostic performance of the physicians. OBJECTIVE This study is aimed at developing a CNN for detecting acral lentiginous melanoma (ALM) and investigating whether its implementation can improve the initial decision for ALM detection made by the physicians. METHODS A CNN was trained using 1072 dermoscopic images of acral benign nevi, ALM and intermediate tumours. To investigate whether the implementation of CNN can improve the initial decision for ALM detection, 60 physicians completed a three-stage survey. In Stage I, they were asked for their decisions solely on the basis of dermoscopic images provided to them. In Stage II, they were also provided with clinical information. In Stage III, they were provided with the additional diagnosis and probability predicted by the CNN. RESULTS The accuracy of ALM detection in the participants was 74.7% (95% confidence interval [CI], 72.6-76.8%) in Stage I and 79.0% (95% CI, 76.7-81.2%) in Stage II. In Stage III, it was 86.9% (95% CI, 85.3-88.4%), which exceeds the accuracy delivered in Stage I by 12.2%p (95% CI, 10.1-14.3%p) and Stage II by 7.9%p (95% CI, 6.0-9.9%p). Moreover, the concordance between the participants considerably increased (Fleiss-κ of 0.436 [95% CI, 0.437-0.573] in Stage I, 0.506 [95% CI, 0.621-0.749] in Stage II and 0.684 [95% CI, 0.621-0.749] in Stage III). CONCLUSIONS Augmented decision-making improved the performance of and concordance between the clinical decisions of a diverse group of experts. This study demonstrates the potential use of CNNs as an adjoining, decision-supporting system for physicians' decisions.
Collapse
Affiliation(s)
- S Lee
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea.,Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Y S Chu
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - S K Yoo
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| | - S Choi
- Department of Dermatology and Cutaneous Biology Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - S J Choe
- Department of Dermatology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - S B Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - K Y Chung
- Department of Dermatology and Cutaneous Biology Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - L Xing
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - B Oh
- Department of Dermatology and Cutaneous Biology Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - S Yang
- Department of Biomedical Engineering, Yonsei University, Wonju, Korea
| |
Collapse
|
17
|
Han SS, Moon IJ, Lim W, Suh IS, Lee SY, Na JI, Kim SH, Chang SE. Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. JAMA Dermatol 2020; 156:29-37. [PMID: 31799995 PMCID: PMC6902187 DOI: 10.1001/jamadermatol.2019.3807] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 10/14/2019] [Indexed: 02/06/2023]
Abstract
Importance Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results. Objective To evaluate whether an algorithm can automatically locate suspected areas and predict the probability of a lesion being malignant. Design, Setting, and Participants Region-based convolutional neural network technology was used to create 924 538 possible lesions by extracting nodular benign lesions from 182 348 clinical photographs. After manually or automatically annotating these possible lesions based on image findings, convolutional neural networks were trained with 1 106 886 image crops to locate and diagnose cancer. Validation data sets (2844 images from 673 patients; mean [SD] age, 58.2 [19.9] years; 308 men [45.8%]; 185 patients with malignant tumors, 305 with benign tumors, and 183 free of tumor) were obtained from 3 hospitals between January 1, 2010, and September 30, 2018. Main Outcomes and Measures The area under the receiver operating characteristic curve, F1 score (mean of precision and recall; range, 0.000-1.000), and Youden index score (sensitivity + specificity -1; 0%-100%) were used to compare the performance of the algorithm with that of the participants. Results The algorithm analyzed a mean (SD) of 4.2 (2.4) photographs per patient and reported the malignancy score according to the highest malignancy output. The area under the receiver operating characteristic curve for the validation data set (673 patients) was 0.910. At a high-sensitivity cutoff threshold, the sensitivity and specificity of the model with the 673 patients were 76.8% and 90.6%, respectively. With the test partition (325 images; 80 patients), the performance of the algorithm was compared with the performance of 13 board-certified dermatologists, 34 dermatology residents, 20 nondermatologic physicians, and 52 members of the general public with no medical background. When the disease screening performance was evaluated at high sensitivity areas using the F1 score and Youden index score, the algorithm showed a higher F1 score (0.831 vs 0.653 [0.126], P < .001) and Youden index score (0.675 vs 0.417 [0.124], P < .001) than that of nondermatologic physicians. The accuracy of the algorithm was comparable with that of dermatologists (F1 score, 0.831 vs 0.835 [0.040]; Youden index score, 0.675 vs 0.671 [0.100]). Conclusions and Relevance The results of the study suggest that the algorithm could localize and diagnose skin cancer without preselection of suspicious lesions by dermatologists.
Collapse
Affiliation(s)
| | - Ik Jun Moon
- Department of Dermatology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | | | - In Suck Suh
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sam Yong Lee
- Department of Plastic and Reconstructive Surgery, Chonnam National University Medical School, Gwangju, Korea
| | - Jung-Im Na
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Seong Hwan Kim
- Department of Plastic and Reconstructive Surgery, Kangnam Sacred Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| |
Collapse
|