1
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Boulogne LH, Lorenz J, Kienzle D, Schön R, Ludwig K, Lienhart R, Jégou S, Li G, Chen C, Wang Q, Shi D, Maniparambil M, Müller D, Mertes S, Schröter N, Hellmann F, Elia M, Dirks I, Bossa MN, Berenguer AD, Mukherjee T, Vandemeulebroucke J, Sahli H, Deligiannis N, Gonidakis P, Huynh ND, Razzak I, Bouadjenek R, Verdicchio M, Borrelli P, Aiello M, Meakin JA, Lemm A, Russ C, Ionasec R, Paragios N, van Ginneken B, Revel MP. The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. Med Image Anal 2024; 97:103230. [PMID: 38875741 DOI: 10.1016/j.media.2024.103230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 01/11/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024]
Abstract
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.
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Affiliation(s)
- Luuk H Boulogne
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands.
| | - Julian Lorenz
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany.
| | - Daniel Kienzle
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Robin Schön
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Katja Ludwig
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Rainer Lienhart
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | | | - Guang Li
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China.
| | - Cong Chen
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Qi Wang
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Derik Shi
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Mayug Maniparambil
- ML-Labs, Dublin City University, N210, Marconi building, Dublin City University, Glasnevin, Dublin 9, Ireland.
| | - Dominik Müller
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany; Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Silvan Mertes
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Niklas Schröter
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Fabio Hellmann
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Miriam Elia
- Faculty of Applied Computer Science, University of Augsburg, Germany.
| | - Ine Dirks
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium.
| | - Matías Nicolás Bossa
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Abel Díaz Berenguer
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Tanmoy Mukherjee
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Hichem Sahli
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Nikos Deligiannis
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Panagiotis Gonidakis
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | | | - Imran Razzak
- University of New South Wales, Sydney, Australia.
| | | | | | | | | | - James A Meakin
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands
| | - Alexander Lemm
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Christoph Russ
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Razvan Ionasec
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Nikos Paragios
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China; TheraPanacea, 75004, Paris, France
| | - Bram van Ginneken
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands
| | - Marie-Pierre Revel
- Department of Radiology, Université de Paris, APHP, Hôpital Cochin, 27 rue du Fg Saint Jacques, 75014 Paris, France
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2
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Kurtansky NR, D'Alessandro BM, Gillis MC, Betz-Stablein B, Cerminara SE, Garcia R, Girundi MA, Goessinger EV, Gottfrois P, Guitera P, Halpern AC, Jakrot V, Kittler H, Kose K, Liopyris K, Malvehy J, Mar VJ, Martin LK, Mathew T, Maul LV, Mothershaw A, Mueller AM, Mueller C, Navarini AA, Rajeswaran T, Rajeswaran V, Saha A, Sashindranath M, Serra-García L, Soyer HP, Theocharis G, Vos A, Weber J, Rotemberg V. The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection. Sci Data 2024; 11:884. [PMID: 39143096 PMCID: PMC11324883 DOI: 10.1038/s41597-024-03743-w] [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: 06/03/2024] [Accepted: 08/05/2024] [Indexed: 08/16/2024] Open
Abstract
AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection.
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Affiliation(s)
- Nicholas R Kurtansky
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
| | | | - Maura C Gillis
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Brigid Betz-Stablein
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | - Sara E Cerminara
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Rafael Garcia
- Computer Vision and Robotics Institute, University of Girona, Girona, Spain
| | | | | | - Philippe Gottfrois
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Pascale Guitera
- Melanoma Institute Australia, Sydney, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Allan C Halpern
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | | | - Harald Kittler
- ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Kivanc Kose
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | | | - Josep Malvehy
- Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Victoria J Mar
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Victorian Melanoma Service, Alfred Hospital, 55 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Linda K Martin
- Melanoma Institute Australia, Sydney, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales, Sydney, Australia
| | | | - Lara Valeska Maul
- Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland
| | - Adam Mothershaw
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | - Alina M Mueller
- Department of Dermatology, University Hospital of Basel, Basel, Switzerland
| | - Christoph Mueller
- ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | | | | | | | - Anup Saha
- Computer Vision and Robotics Institute, University of Girona, Girona, Spain
| | - Maithili Sashindranath
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | | | - H Peter Soyer
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | | | - Ayesha Vos
- Victorian Melanoma Service, Alfred Hospital, 55 Commercial Road, Melbourne, VIC, 3004, Australia
| | - Jochen Weber
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Veronica Rotemberg
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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3
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Tognetti L, Miracapillo C, Leonardelli S, Luschi A, Iadanza E, Cevenini G, Rubegni P, Cartocci A. Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present. Bioengineering (Basel) 2024; 11:758. [PMID: 39199716 PMCID: PMC11351129 DOI: 10.3390/bioengineering11080758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 07/15/2024] [Accepted: 07/17/2024] [Indexed: 09/01/2024] Open
Abstract
There has been growing scientific interest in the research field of deep learning techniques applied to skin cancer diagnosis in the last decade. Though encouraging data have been globally reported, several discrepancies have been observed in terms of study methodology, result presentations and validation in clinical settings. The present review aimed to screen the scientific literature on the application of DL techniques to dermoscopic melanoma/nevi differential diagnosis and extrapolate those original studies adequately by reporting on a DL model, comparing them among clinicians and/or another DL architecture. The second aim was to examine those studies together according to a standard set of statistical measures, and the third was to provide dermatologists with a comprehensive explanation and definition of the most used artificial intelligence (AI) terms to better/further understand the scientific literature on this topic and, in parallel, to be updated on the newest applications in the medical dermatologic field, along with a historical perspective. After screening nearly 2000 records, a subset of 54 was selected. Comparing the 20 studies reporting on convolutional neural network (CNN)/deep convolutional neural network (DCNN) models, we have a scenario of highly performant DL algorithms, especially in terms of low false positive results, with average values of accuracy (83.99%), sensitivity (77.74%), and specificity (80.61%). Looking at the comparison with diagnoses by clinicians (13 studies), the main difference relies on the specificity values, with a +15.63% increase for the CNN/DCNN models (average specificity of 84.87%) compared to humans (average specificity of 64.24%) with a 14,85% gap in average accuracy; the sensitivity values were comparable (79.77% for DL and 79.78% for humans). To obtain higher diagnostic accuracy and feasibility in clinical practice, rather than in experimental retrospective settings, future DL models should be based on a large dataset integrating dermoscopic images with relevant clinical and anamnestic data that is prospectively tested and adequately compared with physicians.
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Affiliation(s)
- Linda Tognetti
- Dermatology Unit, Deparment of Medical, Surgical and Neurosciences, University of Siena, Viale Bracci 16, 53100 Siena, Italy (P.R.)
| | - Chiara Miracapillo
- Dermatology Unit, Deparment of Medical, Surgical and Neurosciences, University of Siena, Viale Bracci 16, 53100 Siena, Italy (P.R.)
| | - Simone Leonardelli
- Dermatology Unit, Deparment of Medical, Surgical and Neurosciences, University of Siena, Viale Bracci 16, 53100 Siena, Italy (P.R.)
| | - Alessio Luschi
- Bioengineering and Biomedical Data Science Lab, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy (E.I.)
| | - Ernesto Iadanza
- Bioengineering and Biomedical Data Science Lab, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy (E.I.)
| | - Gabriele Cevenini
- Bioengineering and Biomedical Data Science Lab, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy (E.I.)
| | - Pietro Rubegni
- Dermatology Unit, Deparment of Medical, Surgical and Neurosciences, University of Siena, Viale Bracci 16, 53100 Siena, Italy (P.R.)
| | - Alessandra Cartocci
- Dermatology Unit, Deparment of Medical, Surgical and Neurosciences, University of Siena, Viale Bracci 16, 53100 Siena, Italy (P.R.)
- Bioengineering and Biomedical Data Science Lab, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy (E.I.)
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4
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Weir VR, Dempsey K, Gichoya JW, Rotemberg V, Wong AKI. A survey of skin tone assessment in prospective research. NPJ Digit Med 2024; 7:191. [PMID: 39014060 PMCID: PMC11252344 DOI: 10.1038/s41746-024-01176-8] [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: 01/20/2024] [Accepted: 06/21/2024] [Indexed: 07/18/2024] Open
Abstract
Increasing evidence supports reduced accuracy of noninvasive assessment tools, such as pulse oximetry, temperature probes, and AI skin diagnosis benchmarks, in patients with darker skin tones. The FDA is exploring potential strategies for device regulation to improve performance across diverse skin tones by including skin tone criteria. However, there is no consensus about how prospective studies should perform skin tone assessment in order to take this bias into account. There are several tools available to conduct skin tone assessments including administered visual scales (e.g., Fitzpatrick Skin Type, Pantone, Monk Skin Tone) and color measurement tools (e.g., reflectance colorimeters, reflectance spectrophotometers, cameras), although none are consistently used or validated across multiple medical domains. Accurate and consistent skin tone measurement depends on many factors including standardized environments, lighting, body parts assessed, patient conditions, and choice of skin tone assessment tool(s). As race and ethnicity are inadequate proxies for skin tone, these considerations can be helpful in standardizing the effect of skin tone on studies such as AI dermatology diagnoses, pulse oximetry, and temporal thermometers. Skin tone bias in medical devices is likely due to systemic factors that lead to inadequate validation across diverse skin tones. There is an opportunity for researchers to use skin tone assessment methods with standardized considerations in prospective studies of noninvasive tools that may be affected by skin tone. We propose considerations that researchers must take in order to improve device robustness to skin tone bias.
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Affiliation(s)
- Vanessa R Weir
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katelyn Dempsey
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - An-Kwok Ian Wong
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA.
- Department of Biostatistics and Bioinformatics, Division of Translational Biomedical Informatics, Duke University, Durham, NC, USA.
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5
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Szymaszek P, Tyszka-Czochara M, Ortyl J. Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence. Molecules 2024; 29:3164. [PMID: 38999115 PMCID: PMC11243723 DOI: 10.3390/molecules29133164] [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] [Received: 05/23/2024] [Revised: 06/14/2024] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.
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Affiliation(s)
- Patryk Szymaszek
- Department of Biotechnology and Physical Chemistry, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland
| | | | - Joanna Ortyl
- Department of Biotechnology and Physical Chemistry, Faculty of Chemical Engineering and Technology, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland
- Photo HiTech Ltd., Bobrzyńskiego 14, 30-348 Kraków, Poland
- Photo4Chem Ltd., Juliusza Lea 114/416A-B, 31-133 Cracow, Poland
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6
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Wen D, Soltan A, Trucco E, Matin RN. From data to diagnosis: skin cancer image datasets for artificial intelligence. Clin Exp Dermatol 2024; 49:675-685. [PMID: 38549552 DOI: 10.1093/ced/llae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/11/2024] [Accepted: 03/25/2024] [Indexed: 06/26/2024]
Abstract
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritized groups in existing datasets are highlighted, which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques that may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.
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Affiliation(s)
- David Wen
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK
| | - Andrew Soltan
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford Cancer and Haematology Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Emanuele Trucco
- VAMPIRE Project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Rubeta N Matin
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Artificial Intelligence Working Party Group, British Association of Dermatologists, London, UK
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7
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Papachristou P, Söderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, Anderson CD, Falk M. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. Br J Dermatol 2024; 191:125-133. [PMID: 38234043 DOI: 10.1093/bjd/ljae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/12/2024] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics. OBJECTIVES To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. METHODS This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data. RESULTS In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. CONCLUSIONS The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.
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Affiliation(s)
- Panagiotis Papachristou
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - My Söderholm
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Ekholmen Primary Healthcare Centre, Region Östergötland, Linköping, Sweden
| | - Jon Pallon
- Department of Clinical Sciences in Malmö, Family Medicine, Lund University, Malmö, Sweden
- Department of Research and Development, Region Kronoberg, Växjö, Sweden
| | - Marina Taloyan
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - Sam Polesie
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Chris D Anderson
- Department of Biomedical and Clinical Sciences, Division of Dermatology and Venereology, Linköping University, Linköping, Sweden
| | - Magnus Falk
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Region Östergötland, Kärna Primary Healthcare Centre, Linköping, Sweden
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8
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Hernández-Pérez C, Combalia M, Podlipnik S, Codella NCF, Rotemberg V, Halpern AC, Reiter O, Carrera C, Barreiro A, Helba B, Puig S, Vilaplana V, Malvehy J. BCN20000: Dermoscopic Lesions in the Wild. Sci Data 2024; 11:641. [PMID: 38886204 PMCID: PMC11183228 DOI: 10.1038/s41597-024-03387-w] [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] [Received: 02/19/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024] Open
Abstract
Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.
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Affiliation(s)
- Carlos Hernández-Pérez
- Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Marc Combalia
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
| | - Sebastian Podlipnik
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
| | - Noel C F Codella
- IBM Research AI, T Watson Research Center, Yorktown Heights, NY, USA
| | - Veronica Rotemberg
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allan C Halpern
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ofer Reiter
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cristina Carrera
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
| | - Alicia Barreiro
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
| | | | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
| | - Veronica Vilaplana
- Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain.
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
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9
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Bryant AK, Zamora‐Resendiz R, Dai X, Morrow D, Lin Y, Jungles KM, Rae JM, Tate A, Pearson AN, Jiang R, Fritsche L, Lawrence TS, Zou W, Schipper M, Ramnath N, Yoo S, Crivelli S, Green MD. Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances. Cancer Med 2024; 13:e7253. [PMID: 38899720 PMCID: PMC11187737 DOI: 10.1002/cam4.7253] [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] [Received: 08/17/2023] [Revised: 02/05/2024] [Accepted: 04/28/2024] [Indexed: 06/21/2024] Open
Abstract
PURPOSE Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. METHODS We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. RESULTS Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. CONCLUSIONS Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
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Affiliation(s)
- Alex K. Bryant
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Rafael Zamora‐Resendiz
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Xin Dai
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Destinee Morrow
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Yuewei Lin
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Kassidy M. Jungles
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - James M. Rae
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Internal MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Akshay Tate
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ashley N. Pearson
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Ralph Jiang
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Lars Fritsche
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
| | - Theodore S. Lawrence
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Weiping Zou
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
- Center of Excellence for Cancer Immunology and ImmunotherapyUniversity of Michigan Rogel Cancer CenterAnn ArborMichiganUSA
- Department of PathologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
| | - Matthew Schipper
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of PharmacologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
| | - Nithya Ramnath
- Division of Hematology Oncology, Department of MedicineUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Division of Hematology Oncology, Department of MedicineVeterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National LaboratoryUptonNew YorkUSA
| | - Silvia Crivelli
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Michael D. Green
- Department of Radiation OncologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
- Department of Radiation Oncology, Veterans Affairs Ann Arbor Healthcare SystemAnn ArborMichiganUSA
- Graduate Program in ImmunologyUniversity of MichiganAnn ArborMichiganUSA
- Graduate Program in Cancer BiologyUniversity of MichiganAnn ArborMichiganUSA
- Department of Microbiology and ImmunologyUniversity of Michigan School of MedicineAnn ArborMichiganUSA
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10
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Primiero CA, Rezze GG, Caffery LJ, Carrera C, Podlipnik S, Espinosa N, Puig S, Janda M, Soyer HP, Malvehy J. A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography. J Invest Dermatol 2024; 144:1200-1207. [PMID: 38231164 DOI: 10.1016/j.jid.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/19/2023] [Accepted: 11/09/2023] [Indexed: 01/18/2024]
Abstract
Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.
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Affiliation(s)
- Clare A Primiero
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia
| | - Gisele Gargantini Rezze
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Liam J Caffery
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia; Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia; Centre for Online Health, The University of Queensland, Brisbane, Australia
| | - Cristina Carrera
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medicine Department, University of Barcelona, Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Sebastian Podlipnik
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Natalia Espinosa
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Susana Puig
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medicine Department, University of Barcelona, Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Monika Janda
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - H Peter Soyer
- Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, Australia; Dermatology Department, Princess Alexandra Hospital, Brisbane, Australia
| | - Josep Malvehy
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica - Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medicine Department, University of Barcelona, Barcelona, Spain; CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain.
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11
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Salinas MP, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, Rotemberg V, Briones J, Mery D, Navarrete-Dechent C. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. NPJ Digit Med 2024; 7:125. [PMID: 38744955 PMCID: PMC11094047 DOI: 10.1038/s41746-024-01103-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/04/2024] [Indexed: 05/16/2024] Open
Abstract
Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.
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Affiliation(s)
- Maria Paz Salinas
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javiera Sepúlveda
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Leonel Hidalgo
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominga Peirano
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Morel
- Universidad Catolica-Evidence Center, Cochrane Chile Associated Center, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Pablo Uribe
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
- Melanoma and Skin Cancer Unit, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Veronica Rotemberg
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Juan Briones
- Department of Oncology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Domingo Mery
- Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Navarrete-Dechent
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Melanoma and Skin Cancer Unit, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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12
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Kim C, Gadgil SU, DeGrave AJ, Omiye JA, Cai ZR, Daneshjou R, Lee SI. Transparent medical image AI via an image-text foundation model grounded in medical literature. Nat Med 2024; 30:1154-1165. [PMID: 38627560 DOI: 10.1038/s41591-024-02887-x] [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: 06/09/2023] [Accepted: 02/27/2024] [Indexed: 04/21/2024]
Abstract
Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.
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Affiliation(s)
- Chanwoo Kim
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Soham U Gadgil
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Alex J DeGrave
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Jesutofunmi A Omiye
- Department of Dermatology, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA
| | - Zhuo Ran Cai
- Program for Clinical Research and Technology, Stanford University, Stanford, CA, USA
| | - Roxana Daneshjou
- Department of Dermatology, Stanford School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford School of Medicine, Stanford, CA, USA.
| | - Su-In Lee
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
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13
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Brancaccio G, Balato A, Malvehy J, Puig S, Argenziano G, Kittler H. Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check. J Invest Dermatol 2024; 144:492-499. [PMID: 37978982 DOI: 10.1016/j.jid.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/08/2023] [Accepted: 10/01/2023] [Indexed: 11/19/2023]
Abstract
The field of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the realm of image-based diagnostic medicine. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy. Although these AI-based applications can operate both autonomously and under human supervision, the best results are achieved through a collaborative approach that leverages the expertise of both AI and human experts. However, it is important to note that most studies focus on assessing the diagnostic accuracy of AI in artificial settings rather than in real-world scenarios. Consequently, the practical utility of AI-assisted diagnosis in a clinical environment is still largely unknown. Furthermore, there exists a knowledge gap concerning the optimal use cases and deployment settings for these AI systems as well as the practical challenges that may arise from widespread implementation. This review explores the advantages and limitations of AI in a variety of real-world contexts, with a specific focus on its value to consumers, general practitioners, and dermatologists.
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Affiliation(s)
| | - Anna Balato
- Dermatology Unit, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, Instituto de Investigaciones Biomédicas August Pi i Sunye, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Barcelona, Spain
| | | | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
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14
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Foltz EA, Witkowski A, Becker AL, Latour E, Lim JY, Hamilton A, Ludzik J. Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review. Cancers (Basel) 2024; 16:629. [PMID: 38339380 PMCID: PMC10854803 DOI: 10.3390/cancers16030629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/28/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. METHODS Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. RESULTS A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. CONCLUSION AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.
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Affiliation(s)
- Emilie A. Foltz
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Alexander Witkowski
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
| | - Alyssa L. Becker
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- John A. Burns School of Medicine, University of Hawai’i at Manoa, Honolulu, HI 96813, USA
| | - Emile Latour
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Jeong Youn Lim
- Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Andrew Hamilton
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
| | - Joanna Ludzik
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
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15
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Sangers TE, Kittler H, Blum A, Braun RP, Barata C, Cartocci A, Combalia M, Esdaile B, Guitera P, Haenssle HA, Kvorning N, Lallas A, Navarrete-Dechent C, Navarini AA, Podlipnik S, Rotemberg V, Soyer HP, Tognetti L, Tschandl P, Malvehy J. Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease. J Eur Acad Dermatol Venereol 2024; 38:22-30. [PMID: 37766502 DOI: 10.1111/jdv.19521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. OBJECTIVE This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection. METHODS An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. RESULTS Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. CONCLUSIONS The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.
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Affiliation(s)
- Tobias E Sangers
- Department of Dermatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Andreas Blum
- Public, Private and Teaching Practice of Dermatology Konstanz, Konstanz, Germany
| | - Ralph P Braun
- Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
| | - Catarina Barata
- Institute for Systems and Robotics, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Ben Esdaile
- Department of Dermatology, Whittington NHS Trust, London, UK
| | - Pascale Guitera
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia
| | - Holger A Haenssle
- Department of Dermatology, Heidelberg University Medical Center, Heidelberg, Germany
| | - Niels Kvorning
- Department of Plastic Surgery, Herlev Hospital, Herlev, Denmark
| | - Aimilios Lallas
- First Department of Dermatology, Faculty of Health Sciences, School of Medicine, Aristotle University, Thessaloniki, Greece
| | - Cristian Navarrete-Dechent
- Melanoma and Skin Cancer Unit, Department of Dermatology, Escuela de Medicina, Pontifica Universidad Catolica de Chile, Santiago, Chile
| | - Alexander A Navarini
- Department of Dermatology and Department of Biomedical Engineering, University Hospital of Basel, Basel, Switzerland
| | - Sebastian Podlipnik
- Department of Dermatology, Hospital Clínic, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Veronica Rotemberg
- Division of Dermatology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - H Peter Soyer
- Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Linda Tognetti
- Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy
| | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
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16
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Almufareh MF, Tariq N, Humayun M, Khan FA. Melanoma identification and classification model based on fine-tuned convolutional neural network. Digit Health 2024; 10:20552076241253757. [PMID: 38798885 PMCID: PMC11119457 DOI: 10.1177/20552076241253757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 04/11/2024] [Indexed: 05/29/2024] Open
Abstract
Background Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer. Materials and Methodology This paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification. Results The proposed CNN model achieves high accuracy demonstrates the model's effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model's efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets. Conclusion Our study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.
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Affiliation(s)
- Maram F Almufareh
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah Al Jouf, Saudi Arabia
| | - Noshina Tariq
- Department of Avionics Engineering, Air University, Islamabad Pakistan
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah Al Jouf, Saudi Arabia
| | - Farrukh Aslam Khan
- Center of Excellence in Information Assurance, King Saud University, Riyadh Saudi Arabia
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17
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Ali Shah A, Shaker ASA, Jabbar S, Abbas Q, Al-Balawi TS, Celebi ME. An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma. Sci Rep 2023; 13:22251. [PMID: 38097641 PMCID: PMC10721601 DOI: 10.1038/s41598-023-49075-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.
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Affiliation(s)
- Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad, Pakistan
| | | | - Sohail Jabbar
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia
| | - Qaisar Abbas
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia.
| | - Talal Saad Al-Balawi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia
| | - M Emre Celebi
- Department of Computer Science and Engineering, University of Central Arkansas, 201 Donaghey Ave., Conway, AR, 72035, USA
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18
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Higgins H, Nakhla A, Lotfalla A, Khalil D, Doshi P, Thakkar V, Shirini D, Bebawy M, Ammari S, Lopci E, Schwartz LH, Postow M, Dercle L. Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma. Diagnostics (Basel) 2023; 13:3483. [PMID: 37998619 PMCID: PMC10670510 DOI: 10.3390/diagnostics13223483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023] Open
Abstract
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.
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Affiliation(s)
- Hayley Higgins
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Abanoub Nakhla
- Department of Clinical Medicine, American University of the Caribbean School of Medicine, 33027 Cupecoy, Sint Maarten, The Netherlands;
| | - Andrew Lotfalla
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - David Khalil
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Parth Doshi
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Vandan Thakkar
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Dorsa Shirini
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
| | - Maria Bebawy
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Samy Ammari
- Département d’Imagerie Médicale Biomaps, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France;
- ELSAN Département de Radiologie, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Lawrence H. Schwartz
- Department of Radiology, New York-Presbyterian, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Michael Postow
- Melanoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Laurent Dercle
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
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19
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Ricci Lara MA, Rodríguez Kowalczuk MV, Lisa Eliceche M, Ferraresso MG, Luna DR, Benitez SE, Mazzuoccolo LD. A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population. Sci Data 2023; 10:712. [PMID: 37853053 PMCID: PMC10584927 DOI: 10.1038/s41597-023-02630-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients.
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Affiliation(s)
- María Agustina Ricci Lara
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina.
- Universidad Tecnológica Nacional, Av. Medrano 951, 1179, Ciudad Autónoma de, Buenos Aires, Argentina.
| | - María Victoria Rodríguez Kowalczuk
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Maite Lisa Eliceche
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - María Guillermina Ferraresso
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Daniel Roberto Luna
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
- Instituto de Medicina Traslacional e Ingeniería Biomédica (IMTIB), UE de triple dependencia CONICET- Instituto Universitario del Hospital Italiano (IUHI) - Hospital ITaliano (HIBA), Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Sonia Elizabeth Benitez
- Departamento de Informática en Salud, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
- Instituto Universitario del Hospital Italiano, Potosí 4265, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
| | - Luis Daniel Mazzuoccolo
- Servicio de Dermatología, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, 1199, Ciudad Autónoma de, Buenos Aires, Argentina
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20
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Barata C, Rotemberg V, Codella NCF, Tschandl P, Rinner C, Akay BN, Apalla Z, Argenziano G, Halpern A, Lallas A, Longo C, Malvehy J, Puig S, Rosendahl C, Soyer HP, Zalaudek I, Kittler H. A reinforcement learning model for AI-based decision support in skin cancer. Nat Med 2023; 29:1941-1946. [PMID: 37501017 PMCID: PMC10427421 DOI: 10.1038/s41591-023-02475-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 06/28/2023] [Indexed: 07/29/2023]
Abstract
We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms.
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Affiliation(s)
- Catarina Barata
- Institute for Systems and Robotics, LARSyS, Instituto Superior Técnico, Lisbon, Portugal
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Christoph Rinner
- Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Vienna, Austria
| | - Bengu Nisa Akay
- Ankara University School of Medicine, Department of Dermatology, Ankara, Turkey
| | - Zoe Apalla
- Second Department of Dermatology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Allan Halpern
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Aimilios Lallas
- Second Department of Dermatology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Caterina Longo
- Dermatology Unit, University of Modena and Reggio Emilia, Modena, Italy
- Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Centro Oncologico ad Alta Tecnologia Diagnostica-Dermatologia, Reggio Emilia, Italy
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Cliff Rosendahl
- General Practice Clinical Unit, Medical School, The University of Queensland, Brisbane, Queensland, Australia
| | - H Peter Soyer
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | - Iris Zalaudek
- Department of Dermatology, Medical University of Trieste, Trieste, Italy
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria.
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21
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Riazi Esfahani P, Mazboudi P, Reddy AJ, Farasat VP, Guirgus ME, Tak N, Min M, Arakji GH, Patel R. Leveraging Machine Learning for Accurate Detection and Diagnosis of Melanoma and Nevi: An Interdisciplinary Study in Dermatology. Cureus 2023; 15:e44120. [PMID: 37750114 PMCID: PMC10518209 DOI: 10.7759/cureus.44120] [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: 08/24/2023] [Indexed: 09/27/2023] Open
Abstract
This study explores the application of machine learning and deep learning algorithms to facilitate the accurate diagnosis of melanoma, a type of malignant skin cancer, and benign nevi. Leveraging a dataset of 793 dermatological images from the Kaggle online platform (Google LLC, Mountain View, California, United States), we developed a model that can accurately differentiate between these lesions based on their distinctive features. The dataset was divided into training (80%), validation (10%), and testing (10%) sets to optimize model performance and ensure its generalizability. Our findings demonstrate the potential of machine learning algorithms in enhancing the efficiency and accuracy of melanoma and nevi detection, with the developed model exhibiting robust performance metrics. Nonetheless, limitations exist due to the potential lack of comprehensive representation of melanoma and nevi cases in the dataset, and variations in image quality and acquisition methods, which may influence the model's performance in real-world clinical settings. Therefore, further research, validation studies, and integration into clinical practice are necessary to ensure the reliability and generalizability of these models. This study underscores the promise of artificial intelligence in advancing dermatologic diagnostics, aiming to improve patient outcomes by supporting early detection and treatment initiation for melanoma.
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Affiliation(s)
| | - Pasha Mazboudi
- Medicine, California University of Science and Medicine, Colton, USA
| | - Akshay J Reddy
- Medicine, California University of Science and Medicine, Colton, USA
| | | | - Monica E Guirgus
- Medicine, California University of Science and Medicine, Colton, USA
| | - Nathaniel Tak
- Medicine, Midwestern University Arizona College of Osteopathic Medicine, Glendale, USA
| | - Mildred Min
- Dermatology, California Northstate University College of Medicine, Elk Grove, USA
| | - Gordon H Arakji
- Health Sciences, California Northstate University, Rancho Cordova, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University Quillen College of Medicine, Johnson City, USA
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22
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Marchetti MA, Cowen EA, Kurtansky NR, Weber J, Dauscher M, DeFazio J, Deng L, Dusza SW, Haliasos H, Halpern AC, Hosein S, Nazir ZH, Marghoob AA, Quigley EA, Salvador T, Rotemberg VM. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). NPJ Digit Med 2023; 6:127. [PMID: 37438476 DOI: 10.1038/s41746-023-00872-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 06/26/2023] [Indexed: 07/14/2023] Open
Abstract
The use of artificial intelligence (AI) has the potential to improve the assessment of lesions suspicious of melanoma, but few clinical studies have been conducted. We validated the accuracy of an open-source, non-commercial AI algorithm for melanoma diagnosis and assessed its potential impact on dermatologist decision-making. We conducted a prospective, observational clinical study to assess the diagnostic accuracy of the AI algorithm (ADAE) in predicting melanoma from dermoscopy skin lesion images. The primary aim was to assess the reliability of ADAE's sensitivity at a predefined threshold of 95%. Patients who had consented for a skin biopsy to exclude melanoma were eligible. Dermatologists also estimated the probability of melanoma and indicated management choices before and after real-time exposure to ADAE scores. All lesions underwent biopsy. Four hundred thirty-five participants were enrolled and contributed 603 lesions (95 melanomas). Participants had a mean age of 59 years, 54% were female, and 96% were White individuals. At the predetermined 95% sensitivity threshold, ADAE had a sensitivity of 96.8% (95% CI: 91.1-98.9%) and specificity of 37.4% (95% CI: 33.3-41.7%). The dermatologists' ability to assess melanoma risk significantly improved after ADAE exposure (AUC 0.7798 vs. 0.8161, p = 0.042). Post-ADAE dermatologist decisions also had equivalent or higher net benefit compared to biopsying all lesions. We validated the accuracy of an open-source melanoma AI algorithm and showed its theoretical potential for improving dermatology experts' ability to evaluate lesions suspicious of melanoma. Larger randomized trials are needed to fully evaluate the potential of adopting this AI algorithm into clinical workflows.
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Affiliation(s)
- Michael A Marchetti
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily A Cowen
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas R Kurtansky
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jochen Weber
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Megan Dauscher
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer DeFazio
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Liang Deng
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Stephen W Dusza
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Helen Haliasos
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allan C Halpern
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sharif Hosein
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zaeem H Nazir
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ashfaq A Marghoob
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elizabeth A Quigley
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Trina Salvador
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Veronica M Rotemberg
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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23
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Phung M, Muralidharan V, Rotemberg V, Novoa RA, Chiou AS, Sadée CY, Rapaport B, Yekrang K, Bitz J, Gevaert O, Ko JM, Daneshjou R. Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research. J Invest Dermatol 2023; 143:1127-1132. [PMID: 37353282 DOI: 10.1016/j.jid.2023.02.035] [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: 09/22/2022] [Revised: 01/30/2023] [Accepted: 02/15/2023] [Indexed: 06/25/2023]
Abstract
Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by supporting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists.
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Affiliation(s)
- Michelle Phung
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | - Vijaytha Muralidharan
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | | | - Roberto Andres Novoa
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | - Albert Sean Chiou
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | - Christoph Y Sadée
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
| | - Bailie Rapaport
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | - Kiana Yekrang
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | - Jared Bitz
- Stanford University, Stanford, California, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA
| | - Justin Meng Ko
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA
| | - Roxana Daneshjou
- Dermatology Department, Stanford University School of Medicine, Redwood City, California, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA.
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24
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Liutkus J, Kriukas A, Stragyte D, Mazeika E, Raudonis V, Galetzka W, Stang A, Valiukeviciene S. Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses. Diagnostics (Basel) 2023; 13:2139. [PMID: 37443533 DOI: 10.3390/diagnostics13132139] [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: 05/30/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based "You Only Look Once" neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm's decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm's sensitivity and specificity for melanomas were 0.88 (0.71-0.96) and 0.87 (0.76-0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77-0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60-0.90). The algorithm's sensitivity for seborrheic keratoses was 0.52 (0.34-0.69). The smartphone-based "You Only Look Once" neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm's sensitivity for seborrheic keratoses.
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Affiliation(s)
- Jokubas Liutkus
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Arturas Kriukas
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Dominyka Stragyte
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Erikas Mazeika
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
| | - Vidas Raudonis
- Artificial Intelligence Center, Kaunas University of Technology, 51423 Kaunas, Lithuania
| | - Wolfgang Galetzka
- Institute of Medical Informatics, Biometrics and Epidemiology, University Hospital Essen, 45130 Essen, Germany
| | - Andreas Stang
- Institute of Medical Informatics, Biometrics and Epidemiology, University Hospital Essen, 45130 Essen, Germany
| | - Skaidra Valiukeviciene
- Department of Skin and Venereal Diseases, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
- Department of Skin and Venereal Diseases, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, 50161 Kaunas, Lithuania
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25
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Kim C, Gadgil SU, DeGrave AJ, Cai ZR, Daneshjou R, Lee SI. Fostering transparent medical image AI via an image-text foundation model grounded in medical literature. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.07.23291119. [PMID: 37398017 PMCID: PMC10312868 DOI: 10.1101/2023.06.07.23291119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET (Medical cONcept rETriever), which learns how to connect medical images with text and generates dense concept annotations to enable tasks in AI transparency from model auditing to model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones, and imaging modalities. We trained MONET on the basis of 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, outperforming supervised models built on previously concept-annotated dermatology datasets. We demonstrate how MONET enables AI transparency across the entire AI development pipeline from dataset auditing to model auditing to building inherently interpretable models.
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Affiliation(s)
- Chanwoo Kim
- Paul G. Allen School of Computer Science and Engineering, University of Washington
| | - Soham U Gadgil
- Paul G. Allen School of Computer Science and Engineering, University of Washington
| | - Alex J DeGrave
- Paul G. Allen School of Computer Science and Engineering, University of Washington
- Medical Scientist Training Program, University of Washington
| | - Zhuo Ran Cai
- Program for Clinical Research and Technology, Stanford University
| | - Roxana Daneshjou
- Department of Dermatology, Stanford School of Medicine
- Department of Biomedical Data Science, Stanford School of Medicine
| | - Su-In Lee
- Paul G. Allen School of Computer Science and Engineering, University of Washington
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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Grossarth S, Mosley D, Madden C, Ike J, Smith I, Huo Y, Wheless L. Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods. Curr Oncol Rep 2023; 25:635-645. [PMID: 37000340 PMCID: PMC10339689 DOI: 10.1007/s11912-023-01407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/01/2023]
Abstract
PURPOSE OF REVIEW The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma. RECENT FINDINGS Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.
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Affiliation(s)
- Sarah Grossarth
- Quillen College of Medicine, East Tennessee State University, Johnson City, TN, USA
| | | | - Christopher Madden
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- State University of New York Downstate College of Medicine, Brooklyn, NY, USA
| | - Jacqueline Ike
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Meharry Medical College, Nashville, TN, USA
| | - Isabelle Smith
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Yuankai Huo
- Department of Computer Science and Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Lee Wheless
- Department of Dermatology, Vanderbilt University Medicine Center, Nashville, TN, USA.
- Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Tennessee Valley Healthcare System VA Medical Center, Nashville, TN, USA.
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Smak Gregoor AM, Sangers TE, Eekhof JAH, Howe S, Revelman J, Litjens RJM, Sarac M, Bindels PJE, Bonten T, Wehrens R, Wakkee M. Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): a pilot feasibility study. EClinicalMedicine 2023; 60:102019. [PMID: 37261324 PMCID: PMC10227364 DOI: 10.1016/j.eclinm.2023.102019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 06/02/2023] Open
Abstract
Background Artificial intelligence (AI)-based mobile phone apps (mHealth) have the potential to streamline care for suspicious skin lesions in primary care. This study aims to investigate the conditions and feasibility of a study that incorporates an AI-based app in primary care and evaluates its potential impact. Methods We conducted a pilot feasibility study from November 22nd, 2021 to June 9th, 2022 with a mixed-methods design on implementation of an AI-based mHealth app for skin cancer detection in three primary care practices in the Netherlands (Rotterdam, Leiden and Katwijk). The primary outcome was the inclusion and successful participation rate of patients and general practitioners (GPs). Secondary outcomes were the reasons, facilitators and barriers for successful participation and the potential impact in both pathways for future sample size calculations. Patients were offered use of an AI-based mHealth app before consulting their GP. GPs assessed the patients blinded and then unblinded to the app. Qualitative data included observations and audio-diaries from patients and GPs and focus-groups and interviews with GPs and GP assistants. Findings Fifty patients were included with a median age of 52 years (IQR 33.5-60.3), 64% were female, and 90% had a light skin type. The average patient inclusion rate was 4-6 per GP practice per month and 84% (n = 42) successfully participated. Similarly, in 90% (n = 45 patients) the GPs also successfully completed the study. GPs never changed their working diagnosis, but did change their treatment plan (n = 5) based on the app's assessments. Notably, 54% of patients with a benign skin lesion and low risk rating, indicated that they would be reassured and cancel their GP visit with these results (p < 0.001). Interpretation Our findings suggest that studying implementation of an AI-based mHealth app for detection of skin cancer in the hands of patients or as a diagnostic tool used by GPs in primary care appears feasible. Preliminary results indicate potential to further investigate both intended use settings. Funding SkinVision B.V.
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Affiliation(s)
- Anna M. Smak Gregoor
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Tobias E. Sangers
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Just AH. Eekhof
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands
| | - Sydney Howe
- School of Health Policy and Management, Erasmus University, Rotterdam, the Netherlands
| | - Jeroen Revelman
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Romy JM. Litjens
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Mohammed Sarac
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | | | - Tobias Bonten
- Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands
| | - Rik Wehrens
- School of Health Policy and Management, Erasmus University, Rotterdam, the Netherlands
| | - Marlies Wakkee
- Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
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Zhang L, Tang L, Xia M, Cao G. The application of artificial intelligence in glaucoma diagnosis and prediction. Front Cell Dev Biol 2023; 11:1173094. [PMID: 37215077 PMCID: PMC10192631 DOI: 10.3389/fcell.2023.1173094] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.
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Affiliation(s)
- Linyu Zhang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Li Tang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Min Xia
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Guofan Cao
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
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Senthil Kumar K, Miskovic V, Blasiak A, Sundar R, Pedrocchi ALG, Pearson AT, Prelaj A, Ho D. Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment. Am Soc Clin Oncol Educ Book 2023; 43:e390084. [PMID: 37235822 DOI: 10.1200/edbk_390084] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.
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Affiliation(s)
- Kirthika Senthil Kumar
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Vanja Miskovic
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Agata Blasiak
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Raghav Sundar
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Haematology-Oncology, National University Cancer Institute, National University Hospital
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Gastric Cancer Consortium, Singapore
- NUS Centre for Cancer Research (N2CR), National University of Singapore, Singapore
| | | | - Alexander T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL
- University of Chicago Comprehensive Cancer Center, Chicago, IL
| | - Arsela Prelaj
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Steele L, Tan XL, Olabi B, Gao JM, Tanaka RJ, Williams HC. Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review. J Eur Acad Dermatol Venereol 2023; 37:657-665. [PMID: 36514990 DOI: 10.1111/jdv.18814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 11/09/2022] [Indexed: 12/15/2022]
Abstract
Machine learning (ML) models for skin cancer recognition may have variable performance across different skin phototypes and skin cancer types. Overall performance metrics alone are insufficient to detect poor subgroup performance. We aimed (1) to assess whether studies of ML models reported results separately for different skin phototypes and rarer skin cancers, and (2) to graphically represent the skin cancer training datasets used by current ML models. In this systematic review, we searched PubMed, Embase and CENTRAL. We included all studies in medical journals assessing an ML technique for skin cancer diagnosis that used clinical or dermoscopic images from 1 January 2012 to 22 September 2021. No language restrictions were applied. We considered rarer skin cancers to be skin cancers other than pigmented melanoma, basal cell carcinoma and squamous cell carcinoma. We identified 114 studies for inclusion. Rarer skin cancers were included by 8/114 studies (7.0%), and results for a rarer skin cancer were reported separately in 1/114 studies (0.9%). Performance was reported across all skin phototypes in 1/114 studies (0.9%), but performance was uncertain in skin phototypes I and VI from minimal representation of the skin phototypes in the test dataset (9/3756 and 1/3756, respectively). For training datasets, although public datasets were most frequently used, with the most widely used being the International Skin Imaging Collaboration (ISIC) archive (65/114 studies, 57.0%), the largest datasets were private. Our review identified that most ML models did not report performance separately for rarer skin cancers and different skin phototypes. A degree of variability in ML model performance across subgroups is expected, but the current lack of transparency is not justifiable and risks models being used inappropriately in populations in whom accuracy is low.
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Affiliation(s)
- Lloyd Steele
- Department of Dermatology, The Royal London Hospital, London, UK.,Centre for Cell Biology and Cutaneous Research, Blizard Institute, Queen Mary University of London, London, UK
| | - Xiang Li Tan
- St George's University Hospitals NHS Foundation Trust, London, UK
| | - Bayanne Olabi
- Biosciences Institute, Newcastle University, Newcastle, UK
| | - Jing Mia Gao
- Department of Dermatology, The Royal London Hospital, London, UK
| | - Reiko J Tanaka
- Department of Bioengineering, Imperial College London, London, UK
| | - Hywel C Williams
- Centre of Evidence-Based Dermatology, School of Medicine, University of Nottingham, Nottingham, UK
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Yue G, Wei P, Zhou T, Jiang Q, Yan W, Wang T. Toward Multicenter Skin Lesion Classification Using Deep Neural Network With Adaptively Weighted Balance Loss. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:119-131. [PMID: 36063522 DOI: 10.1109/tmi.2022.3204646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Recently, deep neural network-based methods have shown promising advantages in accurately recognizing skin lesions from dermoscopic images. However, most existing works focus more on improving the network framework for better feature representation but ignore the data imbalance issue, limiting their flexibility and accuracy across multiple scenarios in multi-center clinics. Generally, different clinical centers have different data distributions, which presents challenging requirements for the network's flexibility and accuracy. In this paper, we divert the attention from framework improvement to the data imbalance issue and propose a new solution for multi-center skin lesion classification by introducing a novel adaptively weighted balance (AWB) loss to the conventional classification network. Benefiting from AWB, the proposed solution has the following advantages: 1) it is easy to satisfy different practical requirements by only changing the backbone; 2) it is user-friendly with no tuning on hyperparameters; and 3) it adaptively enables small intraclass compactness and pays more attention to the minority class. Extensive experiments demonstrate that, compared with solutions equipped with state-of-the-art loss functions, the proposed solution is more flexible and more competent for tackling the multi-center imbalanced skin lesion classification task with considerable performance on two benchmark datasets. In addition, the proposed solution is proved to be effective in handling the imbalanced gastrointestinal disease classification task and the imbalanced DR grading task. Code is available at https://github.com/Weipeishan2021.
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Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatol Ther (Heidelb) 2022; 12:2637-2651. [PMID: 36306100 PMCID: PMC9674813 DOI: 10.1007/s13555-022-00833-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/07/2022] [Indexed: 01/07/2023] Open
Abstract
Artificial intelligence (AI) based on machine learning and convolutional neuron networks (CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer is the most common cancer worldwide and melanoma is one of the deadliest forms of cancer. Dermoscopy has improved physicians' diagnostic accuracy for skin cancer recognition but unfortunately it remains comparatively low. AI could provide invaluable aid in the early evaluation and diagnosis of skin cancer. In the last decade, there has been a breakthrough in new research and publications in the field of AI. Studies have shown that CNN algorithms can classify skin lesions from dermoscopic images with superior or at least equivalent performance compared to clinicians. Even though AI algorithms have shown very promising results for the diagnosis of skin cancer in reader studies, their generalizability and applicability in everyday clinical practice remain elusive. Herein we attempted to summarize the potential pitfalls and challenges of AI that were underlined in reader studies and pinpoint strategies to overcome limitations in future studies. Finally, we tried to analyze the advantages and opportunities that lay ahead for a better future for dermatology and patients, with the potential use of AI in our practices.
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Affiliation(s)
- Konstantinos Liopyris
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
- Dermatology Department, Memorial Sloan Kettering Cancer Center, New York, NY, 10021, USA
| | - Stamatios Gregoriou
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece.
| | - Julia Dias
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
| | - Alexandros J Stratigos
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens, 5 Ionos Dragoumi Str, 16121, Athens, Greece
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Ahmadi Mehr R, Ameri A. Skin Cancer Detection Based on Deep Learning. J Biomed Phys Eng 2022; 12:559-568. [PMID: 36569567 PMCID: PMC9759648 DOI: 10.31661/jbpe.v0i0.2207-1517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 10/28/2022] [Indexed: 06/17/2023]
Abstract
Background The conventional procedure of skin-related disease detection is a visual inspection by a dermatologist or a primary care clinician, using a dermatoscope. The suspected patients with early signs of skin cancer are referred for biopsy and histopathological examination to ensure the correct diagnosis and the best treatment. Recent advancements in deep convolutional neural networks (CNNs) have achieved excellent performance in automated skin cancer classification with accuracy similar to that of dermatologists. However, such improvements are yet to bring about a clinically trusted and popular system for skin cancer detection. Objective This study aimed to propose viable deep learning (DL) based method for the detection of skin cancer in lesion images, to help physicians in diagnosis. Material and Methods In this analytical study, a novel DL based model was proposed, in which other than the lesion image, the patient's data, including the anatomical site of the lesion, age, and gender were used as the model input to predict the type of the lesion. An Inception-ResNet-v2 CNN pretrained for object recognition was employed in the proposed model. Results Based on the results, the proposed method achieved promising performance for various skin conditions, and also using the patient's metadata in addition to the lesion image for classification improved the classification accuracy by at least 5% in all cases investigated. On a dataset of 57536 dermoscopic images, the proposed approach achieved an accuracy of 89.3%±1.1% in the discrimination of 4 major skin conditions and 94.5%±0.9% in the classification of benign vs. malignant lesions. Conclusion The promising results highlight the efficacy of the proposed approach and indicate that the inclusion of the patient's metadata with the lesion image can enhance the skin cancer detection performance.
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Affiliation(s)
- Reza Ahmadi Mehr
- MSc, Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Ameri
- PhD, Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Spotlight in Plastic Surgery: October 2022. Plast Reconstr Surg 2022; 150:948-950. [PMID: 36734967 DOI: 10.1097/prs.0000000000009612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs. Animals (Basel) 2022; 12:ani12141755. [PMID: 35883302 PMCID: PMC9311578 DOI: 10.3390/ani12141755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/17/2022] Open
Abstract
Subjective lameness assessment has been a controversial subject given the lack of agreement between observers; this has prompted the development of kinetic and kinematic devices in order to obtain an objective evaluation of locomotor system in dogs. After proper training, neural networks are potentially capable of making a non-human diagnosis of canine lameness. The purpose of this study was to investigate whether artificial neural networks could be used to determine canine hindlimb lameness by computational means only. The outcome of this study could potentially assess the efficacy of certain treatments against diseases that cause lameness. With this aim, input data were obtained from an inertial sensor positioned on the rump. Data from dogs with unilateral hindlimb lameness and sound dogs were used to obtain differences between both groups at walk. The artificial neural network, after necessary adjustments, was integrated into a web management tool, and the preliminary results discriminating between lame and sound dogs are promising. The analysis of spatial data with artificial neural networks was summarized and developed into a web app that has proven to be a useful tool to discriminate between sound and lame dogs. Additionally, this environment allows veterinary clinicians to adequately follow the treatment of lame canine patients.
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