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Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y. Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends. J Clin Med 2022; 11:jcm11226826. [PMID: 36431301 PMCID: PMC9693628 DOI: 10.3390/jcm11226826] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Thanks to the rapid development of computer-based systems and deep-learning-based algorithms, artificial intelligence (AI) has long been integrated into the healthcare field. AI is also particularly helpful in image recognition, surgical assistance and basic research. Due to the unique nature of dermatology, AI-aided dermatological diagnosis based on image recognition has become a modern focus and future trend. Key scientific concepts of review: The use of 3D imaging systems allows clinicians to screen and label skin pigmented lesions and distributed disorders, which can provide an objective assessment and image documentation of lesion sites. Dermatoscopes combined with intelligent software help the dermatologist to easily correlate each close-up image with the corresponding marked lesion in the 3D body map. In addition, AI in the field of prosthetics can assist in the rehabilitation of patients and help to restore limb function after amputation in patients with skin tumors. THE AIM OF THE STUDY For the benefit of patients, dermatologists have an obligation to explore the opportunities, risks and limitations of AI applications. This study focuses on the application of emerging AI in dermatology to aid clinical diagnosis and treatment, analyzes the current state of the field and summarizes its future trends and prospects so as to help dermatologists realize the impact of new technological innovations on traditional practices so that they can embrace and use AI-based medical approaches more quickly.
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Affiliation(s)
- Zhouxiao Li
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | | | - Thilo Ludwig Schenck
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | - Riccardo Enzo Giunta
- Division of Hand, Plastic and Aesthetic Surgery, University Hospital, LMU Munich, 80339 Munich, Germany
| | - Qingfeng Li
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Correspondence: (Q.L.); (Y.S.)
| | - Yangbai Sun
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200023, China
- Correspondence: (Q.L.); (Y.S.)
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2
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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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Biasi LD, Citarella AA, Risi M, Tortora G. A Cloud Approach for Melanoma Detection based on Deep Learning Networks. IEEE J Biomed Health Inform 2021; 26:962-972. [PMID: 34543209 DOI: 10.1109/jbhi.2021.3113609] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In the era of digitized images, the goal is to be able to extract information from them and create new knowledge thanks to the use of Computer Vision techniques, Machine Learning and Deep Learning. This allows their use for early diagnosis and subsequent determination of the treatment of many pathologies. In the specific case treated here, deep neural networks are used in the dermatological field to distinguish between melanoma and non-melanoma images. In this work we have underlined two essential points of melanoma detection research. The first aspect taken into consideration is how even a simple modification of the parameters in the dataset determines a change of the accuracy of the classifiers, while working on the same original dataset. The second point is the need to have a system architecture that can be more flexible in updating the training datasets for the classification of this pathology. In this context, the proposed architecture reserves the goal of developing and implementing a hybrid architecture based on Cloud, Fog and Edge Computing in order to provide a Melanoma Detection service based on clinical and/or dermoscopic images. At the same time, this architecture must be able to interface with the amount of data to be analyzed by reducing the running time of the necessary computational operations. This has been highlighted with experiments carried out on a single machine and on different distribution systems, highlighting how a distributed approach guarantees the achievement of an output in a much more acceptable time without the need to fully rely on data scientists skills.
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Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of Artificial Intelligence in Kidney Disease. Int J Med Sci 2020; 17:970-984. [PMID: 32308551 PMCID: PMC7163364 DOI: 10.7150/ijms.42078] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 03/17/2020] [Indexed: 12/17/2022] Open
Abstract
Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.
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Affiliation(s)
- Qiongjing Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Haixia Zhang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China.,Department of Nephrology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, Jiangsu 215000, China
| | - Tianci Deng
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Shumei Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangning Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Wenbin Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Yanyun Xie
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Huipeng Ge
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiufen Wang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Qiaoling Zhou
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
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Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. JAMA Dermatol 2019; 155:1291-1299. [PMID: 31215969 PMCID: PMC6584889 DOI: 10.1001/jamadermatol.2019.1375] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 04/17/2019] [Indexed: 12/19/2022]
Abstract
IMPORTANCE The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma. OBJECTIVE To critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. DATA SOURCES The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. STUDY SELECTION Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. DATA EXTRACTION AND SYNTHESIS Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. MAIN OUTCOMES AND MEASURES Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes. RESULTS The literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P < .001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P = .67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. CONCLUSIONS AND RELEVANCE Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.
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Affiliation(s)
- Vincent Dick
- ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Christoph Sinz
- ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Martina Mittlböck
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Harald Kittler
- ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Philipp Tschandl
- ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria
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Johansen TH, Møllersen K, Ortega S, Fabelo H, Garcia A, Callico GM, Godtliebsen F. Recent advances in hyperspectral imaging for melanoma detection. WIRES COMPUTATIONAL STATISTICS 2019. [DOI: 10.1002/wics.1465] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Kajsa Møllersen
- Department of Community Medicine UiT The Arctic University of Norway Tromsø Norway
| | - Samuel Ortega
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Himar Fabelo
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Aday Garcia
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Gustavo M. Callico
- Institute for Applied Microelectronics University of Las Palmas de Gran Canaria Las Palmas Spain
| | - Fred Godtliebsen
- Department of Mathematics and Statistics UiT The Arctic University of Norway Tromsø Norway
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Marka A, Carter JB, Toto E, Hassanpour S. Automated detection of nonmelanoma skin cancer using digital images: a systematic review. BMC Med Imaging 2019; 19:21. [PMID: 30819133 PMCID: PMC6394090 DOI: 10.1186/s12880-019-0307-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 01/07/2019] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Computer-aided diagnosis of skin lesions is a growing area of research, but its application to nonmelanoma skin cancer (NMSC) is relatively under-studied. The purpose of this review is to synthesize the research that has been conducted on automated detection of NMSC using digital images and to assess the quality of evidence for the diagnostic accuracy of these technologies. METHODS Eight databases (PubMed, Google Scholar, Embase, IEEE Xplore, Web of Science, SpringerLink, ScienceDirect, and the ACM Digital Library) were searched to identify diagnostic studies of NMSC using image-based machine learning models. Two reviewers independently screened eligible articles. The level of evidence of each study was evaluated using a five tier rating system, and the applicability and risk of bias of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS Thirty-nine studies were reviewed. Twenty-four models were designed to detect basal cell carcinoma, two were designed to detect squamous cell carcinoma, and thirteen were designed to detect both. All studies were conducted in silico. The overall diagnostic accuracy of the classifiers, defined as concordance with histopathologic diagnosis, was high, with reported accuracies ranging from 72 to 100% and areas under the receiver operating characteristic curve ranging from 0.832 to 1. Most studies had substantial methodological limitations, but several were robustly designed and presented a high level of evidence. CONCLUSION Most studies of image-based NMSC classifiers report performance greater than or equal to the reported diagnostic accuracy of the average dermatologist, but relatively few studies have presented a high level of evidence. Clinical studies are needed to assess whether these technologies can feasibly be implemented as a real-time aid for clinical diagnosis of NMSC.
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Affiliation(s)
- Arthur Marka
- Dartmouth Geisel School of Medicine, Box 163, Kellogg Building, 45 Dewey Field Road, Hanover, NH USA
| | - Joi B. Carter
- Section of Dermatology, Dartmouth-Hitchcock Medical Center, Lebanon, NH USA
- Department of Surgery, Dartmouth Geisel School of Medicine, Hanover, NH USA
| | - Ermal Toto
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA USA
| | - Saeed Hassanpour
- Department of Biomedical Data Science, Dartmouth College, Lebanon, NH USA
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Ferrante di Ruffano L, Takwoingi Y, Dinnes J, Chuchu N, Bayliss SE, Davenport C, Matin RN, Godfrey K, O'Sullivan C, Gulati A, Chan SA, Durack A, O'Connell S, Gardiner MD, Bamber J, Deeks JJ, Williams HC. Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. Cochrane Database Syst Rev 2018; 12:CD013186. [PMID: 30521691 PMCID: PMC6517147 DOI: 10.1002/14651858.cd013186] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and cutaneous squamous cell carcinoma (cSCC) are high-risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Computer-assisted diagnosis (CAD) systems use artificial intelligence to analyse lesion data and arrive at a diagnosis of skin cancer. When used in unreferred settings ('primary care'), CAD may assist general practitioners (GPs) or other clinicians to more appropriately triage high-risk lesions to secondary care. Used alongside clinical and dermoscopic suspicion of malignancy, CAD may reduce unnecessary excisions without missing melanoma cases. OBJECTIVES To determine the accuracy of CAD systems for diagnosing cutaneous invasive melanoma and atypical intraepidermal melanocytic variants, BCC or cSCC in adults, and to compare its accuracy with that of dermoscopy. SEARCH METHODS We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles. SELECTION CRITERIA Studies of any design that evaluated CAD alone, or in comparison with dermoscopy, in adults with lesions suspicious for melanoma or BCC or cSCC, and compared with a reference standard of either histological confirmation or clinical follow-up. DATA COLLECTION AND ANALYSIS Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition or diagnostic threshold were missing. We estimated summary sensitivities and specificities separately by type of CAD system, using the bivariate hierarchical model. We compared CAD with dermoscopy using (a) all available CAD data (indirect comparisons), and (b) studies providing paired data for both tests (direct comparisons). We tested the contribution of human decision-making to the accuracy of CAD diagnoses in a sensitivity analysis by removing studies that gave CAD results to clinicians to guide diagnostic decision-making. MAIN RESULTS We included 42 studies, 24 evaluating digital dermoscopy-based CAD systems (Derm-CAD) in 23 study cohorts with 9602 lesions (1220 melanomas, at least 83 BCCs, 9 cSCCs), providing 32 datasets for Derm-CAD and seven for dermoscopy. Eighteen studies evaluated spectroscopy-based CAD (Spectro-CAD) in 16 study cohorts with 6336 lesions (934 melanomas, 163 BCC, 49 cSCCs), providing 32 datasets for Spectro-CAD and six for dermoscopy. These consisted of 15 studies using multispectral imaging (MSI), two studies using electrical impedance spectroscopy (EIS) and one study using diffuse-reflectance spectroscopy. Studies were incompletely reported and at unclear to high risk of bias across all domains. Included studies inadequately address the review question, due to an abundance of low-quality studies, poor reporting, and recruitment of highly selected groups of participants.Across all CAD systems, we found considerable variation in the hardware and software technologies used, the types of classification algorithm employed, methods used to train the algorithms, and which lesion morphological features were extracted and analysed across all CAD systems, and even between studies evaluating CAD systems. Meta-analysis found CAD systems had high sensitivity for correct identification of cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in highly selected populations, but with low and very variable specificity, particularly for Spectro-CAD systems. Pooled data from 22 studies estimated the sensitivity of Derm-CAD for the detection of melanoma as 90.1% (95% confidence interval (CI) 84.0% to 94.0%) and specificity as 74.3% (95% CI 63.6% to 82.7%). Pooled data from eight studies estimated the sensitivity of multispectral imaging CAD (MSI-CAD) as 92.9% (95% CI 83.7% to 97.1%) and specificity as 43.6% (95% CI 24.8% to 64.5%). When applied to a hypothetical population of 1000 lesions at the mean observed melanoma prevalence of 20%, Derm-CAD would miss 20 melanomas and would lead to 206 false-positive results for melanoma. MSI-CAD would miss 14 melanomas and would lead to 451 false diagnoses for melanoma. Preliminary findings suggest CAD systems are at least as sensitive as assessment of dermoscopic images for the diagnosis of invasive melanoma and atypical intraepidermal melanocytic variants. We are unable to make summary statements about the use of CAD in unreferred populations, or its accuracy in detecting keratinocyte cancers, or its use in any setting as a diagnostic aid, because of the paucity of studies. AUTHORS' CONCLUSIONS In highly selected patient populations all CAD types demonstrate high sensitivity, and could prove useful as a back-up for specialist diagnosis to assist in minimising the risk of missing melanomas. However, the evidence base is currently too poor to understand whether CAD system outputs translate to different clinical decision-making in practice. Insufficient data are available on the use of CAD in community settings, or for the detection of keratinocyte cancers. The evidence base for individual systems is too limited to draw conclusions on which might be preferred for practice. Prospective comparative studies are required that evaluate the use of already evaluated CAD systems as diagnostic aids, by comparison to face-to-face dermoscopy, and in participant populations that are representative of those in which the test would be used in practice.
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Affiliation(s)
| | - Yemisi Takwoingi
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Jacqueline Dinnes
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Naomi Chuchu
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
| | - Susan E Bayliss
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
| | - Clare Davenport
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
| | - Rubeta N Matin
- Churchill HospitalDepartment of DermatologyOld RoadHeadingtonOxfordUKOX3 7LE
| | - Kathie Godfrey
- The University of Nottinghamc/o Cochrane Skin GroupNottinghamUK
| | | | - Abha Gulati
- Barts Health NHS TrustDepartment of DermatologyWhitechapelLondonUKE11BB
| | - Sue Ann Chan
- City HospitalBirmingham Skin CentreDudley RdBirminghamUKB18 7QH
| | - Alana Durack
- Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation TrustDermatologyHills RoadCambridgeUKCB2 0QQ
| | - Susan O'Connell
- Cardiff and Vale University Health BoardCEDAR Healthcare Technology Research CentreCardiff Medicentre, University Hospital of Wales, Heath Park CampusCardiffWalesUKCF144UJ
| | | | - Jeffrey Bamber
- Institute of Cancer Research and The Royal Marsden NHS Foundation TrustJoint Department of Physics15 Cotswold RoadSuttonUKSM2 5NG
| | - Jonathan J Deeks
- University of BirminghamInstitute of Applied Health ResearchEdgbaston CampusBirminghamUKB15 2TT
- University Hospitals Birmingham NHS Foundation Trust and University of BirminghamNIHR Birmingham Biomedical Research CentreBirminghamUK
| | - Hywel C Williams
- University of NottinghamCentre of Evidence Based DermatologyQueen's Medical CentreDerby RoadNottinghamUKNG7 2UH
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Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, Watanabe R, Okiyama N, Ohara K, Fujimoto M. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol 2018; 180:373-381. [PMID: 29953582 DOI: 10.1111/bjd.16924] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/13/2018] [Indexed: 01/17/2023]
Abstract
BACKGROUND Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremely large. OBJECTIVES To determine whether deep-learning technology could be used to develop an efficient skin cancer classification system with a relatively small dataset of clinical images. METHODS A deep convolutional neural network (DCNN) was trained using a dataset of 4867 clinical images obtained from 1842 patients diagnosed with skin tumours at the University of Tsukuba Hospital from 2003 to 2016. The images consisted of 14 diagnoses, including both malignant and benign conditions. Its performance was tested against 13 board-certified dermatologists and nine dermatology trainees. RESULTS The overall classification accuracy of the trained DCNN was 76·5%. The DCNN achieved 96·3% sensitivity (correctly classified malignant as malignant) and 89·5% specificity (correctly classified benign as benign). Although the accuracy of malignant or benign classification by the board-certified dermatologists was statistically higher than that of the dermatology trainees (85·3% ± 3·7% and 74·4% ± 6·8%, P < 0·01), the DCNN achieved even greater accuracy, as high as 92·4% ± 2·1% (P < 0·001). CONCLUSIONS We have developed an efficient skin tumour classifier using a DCNN trained on a relatively small dataset. The DCNN classified images of skin tumours more accurately than board-certified dermatologists. Collectively, the current system may have capabilities for screening purposes in general medical practice, particularly because it requires only a single clinical image for classification.
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Affiliation(s)
- Y Fujisawa
- Dermatology Division, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, Japan, 305-8577
| | - Y Otomo
- Kyocera Communications System Co., Ltd, Kyoto, Japan
| | - Y Ogata
- KCCS Mobile Engineering Co., Ltd, Tokyo, Japan
| | - Y Nakamura
- Dermatology Division, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, Japan, 305-8577
| | - R Fujita
- Kyocera Communications System Co., Ltd, Kyoto, Japan
| | - Y Ishitsuka
- Dermatology Division, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, Japan, 305-8577
| | - R Watanabe
- Dermatology Division, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, Japan, 305-8577
| | - N Okiyama
- Dermatology Division, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, Japan, 305-8577
| | - K Ohara
- Dermatology, Akasaka Toranomon Clinic, Tokyo, Japan
| | - M Fujimoto
- Dermatology Division, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, Japan, 305-8577
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10
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Del Rosario F, Farahi JM, Drendel J, Buntinx-Krieg T, Caravaglio J, Domozych R, Chapman S, Braunberger T, Dellavalle RP, Norris DA, Fathi R, Alkousakis T. Performance of a computer-aided digital dermoscopic image analyzer for melanoma detection in 1,076 pigmented skin lesion biopsies. J Am Acad Dermatol 2018; 78:927-934.e6. [PMID: 29678380 DOI: 10.1016/j.jaad.2017.01.049] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 01/27/2017] [Accepted: 01/29/2017] [Indexed: 11/26/2022]
Abstract
BACKGROUND Digital dermoscopic image analysis of pigmented skin lesions (PSLs) has become increasingly popular, despite its unclear clinical utility. Unbiased, high-powered studies investigating the efficacy of commercially available systems are limited. OBJECTIVE To investigate the diagnostic performance of the FotoFinder Mole-Analyzer in assessing PSLs for cutaneous melanoma. METHODS In this 15-year retrospective study, the histopathologies of 1076 biopsied PSLs among a total of 2500 imaged PSLs were collected. The biopsied PSLs were categorized as benign or malignant (cutaneous melanoma) based on histopathology. Analyzer scores (0-1.00) for these PSLs were obtained and grouped according to histopathology. RESULTS At an optimized cutoff score of 0.50, a sensitivity of 56% and a specificity of 74% were achieved. The area under the receiver operating characteristics curve was 0.698, indicating poor accuracy as a diagnostic tool. LIMITATIONS This study had a retrospective design and involved only a single institution. CONCLUSION Our study reveals a low sensitivity of the scoring function of this digital dermoscopic image analyzer for detecting cutaneous melanomas. Physicians must apply keen clinical judgment when using such devices in the screening of suspicious PSLs.
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Affiliation(s)
| | | | | | | | | | - Renee Domozych
- University of Central Florida College of Medicine, Orlando, Florida
| | - Stephanie Chapman
- Michigan State University, College of Human Medicine, Grand Rapids, Michigan
| | - Taylor Braunberger
- University of North Dakota School of Medicine, Grand Forks, North Dakota
| | | | - David A Norris
- University of Colorado School of Medicine, Aurora, Colorado
| | - Ramin Fathi
- University of Colorado School of Medicine, Aurora, Colorado
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Møllersen K, Zortea M, Schopf TR, Kirchesch H, Godtliebsen F. Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images. PLoS One 2017; 12:e0190112. [PMID: 29267358 PMCID: PMC5739481 DOI: 10.1371/journal.pone.0190112] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 12/09/2017] [Indexed: 11/23/2022] Open
Abstract
Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.
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Affiliation(s)
- Kajsa Møllersen
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- * E-mail:
| | - Maciel Zortea
- Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway
| | - Thomas R. Schopf
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
| | | | - Fred Godtliebsen
- Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway
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Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inform 2017; 101:58-67. [DOI: 10.1016/j.ijmedinf.2017.02.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 01/28/2017] [Accepted: 02/04/2017] [Indexed: 12/18/2022]
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