Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologists

NCT04636164 | Terminated

• 1 other identifier: 2020-3233
• Study Type: interventional
• Target: 55
• Locations: 1 country
• Sites: 1

Brief Summary

Background: Deep neural networks (DNN) has been applied to many kinds of skin diseases in experimental settings. 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.

Conditions

Skin Diseases

Keywords

skin diseases; deep neural networks

Outcome Measures

Primary Outcomes (1)

• Top-1 diagnostic accuracy

  frequency of correct Top-1 prediction

  6 consecutive months

Secondary Outcomes (3)

• Top-2 and 3 diagnostic accuracy

  6 consecutive months

• Infection sensitivity

  6 consecutive months

• Malignancy sensitivity

  6 consecutive months

Study Arms (2)

DNN group

EXPERIMENTAL

using deep neural networks for skin lesion diagnosis

Diagnostic Test: Model Dermatology (deep neural networks; Build 2020)

Control group

NO INTERVENTION

conventional diagnosis

Interventions

Model Dermatology (deep neural networks; Build 2020) DIAGNOSTIC_TEST

Physicians in the DNN group take pictures of the skin lesion and use the algorithm by uploading pictures.

DNN group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• non-dermatologist physician (residents) who agree to participate in this study

You may not qualify if:

• dermatology residents
• non-dermatology residents who use other deep neural networks for skin lesion diagnosis

Sponsors & Collaborators

• Pyoeng Gyun Choe: lead

Study Sites (1)

Seoul National University Hospital

Seoul, 03080, South Korea

Location

Related Publications (7)

• 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.

  PMID: 31981517 BACKGROUND

• 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.

  PMID: 31143882 BACKGROUND

• 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.

  PMID: 32424212 BACKGROUND

• 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.

  PMID: 32243882 BACKGROUND

• 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.

  PMID: 15858472 BACKGROUND

• 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.

  PMID: 31255749 BACKGROUND

• 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.

  PMID: 31201137 BACKGROUND

MeSH Terms

Conditions

Skin Diseases

Condition Hierarchy (Ancestors)

Skin and Connective Tissue Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: November 13, 2020
• First Posted: November 19, 2020
• Study Start: November 27, 2020
• Primary Completion: November 27, 2021
• Study Completion: December 27, 2021
• Last Updated: October 27, 2022

Record last verified: 2022-10

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)