Clinical trial NCT04636164: Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologists
Official title: Effect of Using Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologist Physician
Study type: INTERVENTIONAL
Overall status: TERMINATED
Phase: NA
Conditions: Skin Diseases
Keywords: skin diseases
deep neural networks
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
First posted: 2020-11-19
Enrollment: 55
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Detailed description: In the DNN group and control group, these steps are the same process.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: Top-1 diagnostic accuracy
timeFrame: 6 consecutive months
Secondary outcomes:
measure: Top-2 and 3 diagnostic accuracy
timeFrame: 6 consecutive months
measure: Infection sensitivity
timeFrame: 6 consecutive months
measure: Malignancy sensitivity
timeFrame: 6 consecutive months
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Seoul National University Hospital
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.