Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologists
NCT ID: NCT04636164
Last Updated: 2022-10-27
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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TERMINATED
NA
55 participants
INTERVENTIONAL
2020-11-27
2021-12-27
Brief Summary
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Objective: The objective of this study is to confirm the augmentation of deep neural networks for the diagnosis of skin diseases in non-dermatologist physicians in a real-world setting.
Methods: A total of 40 non-dermatologist physicians in a single tertiary care hospital will be enrolled. They will be randomized to a DNN group and control group. By comparing two groups, the investigators will estimate the effect of using deep neural networks on the diagnosis of skin disease in terms of accuracy.
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Detailed Description
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1. Routine exam and capture photographs of skin lesions for all eligible consecutive series patient.
2. Make a clinical diagnosis (BEFORE-DX)
3. Make a clinical diagnosis (AFTER-DX)
4. consult to dermatologist
In the DNN group, after making the BEFORE-DX, physicians use deep neural networks and make an AFTER-DX considering the results of the deep neural networks (Model Dermatology, build 2020).
In the control group, after making the BEFORE-DX, physicians make an AFTER-DX after reviewing the pictures of skin lesions once more.
Ground truth will be based on the biopsy if available, or the consensus diagnosis of the dermatologists.
The investigators will compare the accuracy between the DNN group and control group after 6 consecutive months study.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
NONE
Study Groups
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DNN group
using deep neural networks for skin lesion diagnosis
Model Dermatology (deep neural networks; Build 2020)
Physicians in the DNN group take pictures of the skin lesion and use the algorithm by uploading pictures.
Control group
conventional diagnosis
No interventions assigned to this group
Interventions
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Model Dermatology (deep neural networks; Build 2020)
Physicians in the DNN group take pictures of the skin lesion and use the algorithm by uploading pictures.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* non-dermatology residents who use other deep neural networks for skin lesion diagnosis
ALL
No
Sponsors
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Pyoeng Gyun Choe
OTHER
Responsible Party
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Pyoeng Gyun Choe
Clinical Professor
Locations
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Seoul National University Hospital
Seoul, , South Korea
Countries
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References
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Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22.
Lin H, Li R, Liu Z, Chen J, Yang Y, Chen H, Lin Z, Lai W, Long E, Wu X, Lin D, Zhu Y, Chen C, Wu D, Yu T, Cao Q, Li X, Li J, Li W, Wang J, Yang M, Hu H, Zhang L, Yu Y, Chen X, Hu J, Zhu K, Jiang S, Huang Y, Tan G, Huang J, Lin X, Zhang X, Luo L, Liu Y, Liu X, Cheng B, Zheng D, Wu M, Chen W, Liu Y. Diagnostic Efficacy and Therapeutic Decision-making Capacity of an Artificial Intelligence Platform for Childhood Cataracts in Eye Clinics: A Multicentre Randomized Controlled Trial. EClinicalMedicine. 2019 Mar 17;9:52-59. doi: 10.1016/j.eclinm.2019.03.001. eCollection 2019 Mar.
Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, Kanada K, de Oliveira Marinho G, Gallegos J, Gabriele S, Gupta V, Singh N, Natarajan V, Hofmann-Wellenhof R, Corrado GS, Peng LH, Webster DR, Ai D, Huang SJ, Liu Y, Dunn RC, Coz D. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020 Jun;26(6):900-908. doi: 10.1038/s41591-020-0842-3. Epub 2020 May 18.
Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, Chae JB, Huh CH, Na JI. Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. J Invest Dermatol. 2020 Sep;140(9):1753-1761. doi: 10.1016/j.jid.2020.01.019. Epub 2020 Mar 31.
Sellheyer K, Bergfeld WF. A retrospective biopsy study of the clinical diagnostic accuracy of common skin diseases by different specialties compared with dermatology. J Am Acad Dermatol. 2005 May;52(5):823-30. doi: 10.1016/j.jaad.2004.11.072.
Cui X, Wei R, Gong L, Qi R, Zhao Z, Chen H, Song K, Abdulrahman AAA, Wang Y, Chen JZS, Chen S, Zhao Y, Gao X. Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review. J Am Acad Dermatol. 2019 Nov;81(5):1176-1180. doi: 10.1016/j.jaad.2019.06.042. Epub 2019 Jun 27.
Tschandl P, Codella N, Akay BN, Argenziano G, Braun RP, Cabo H, Gutman D, Halpern A, Helba B, Hofmann-Wellenhof R, Lallas A, Lapins J, Longo C, Malvehy J, Marchetti MA, Marghoob A, Menzies S, Oakley A, Paoli J, Puig S, Rinner C, Rosendahl C, Scope A, Sinz C, Soyer HP, Thomas L, Zalaudek I, Kittler H. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019 Jul;20(7):938-947. doi: 10.1016/S1470-2045(19)30333-X. Epub 2019 Jun 12.
Other Identifiers
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2020-3233
Identifier Type: -
Identifier Source: org_study_id
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