Using AI as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia

NCT04562168 | Completed

• 1 other identifier: P20/159-P
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

Background: Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, specially for skin cancer detection and classification. Objective: The objective of this study is to perform a prospective validation of an image analysis ML model, which is capable of screening 44 different skin disease types, comparing its diagnostic capacity with that of General Practitioners (GPs) and dermatologists. Methods: In this prospective study 100 consecutive patients who visit a participant GP with a skin problem in central Catalonia will be recruited, data collection is planned to last 7 months. Skin diseases anonymized pictures will be taken and introduced in the ML model interface, which will return top 5 accuracy diagnosis. The same image will be also sent as a teledermatology consultation, following the current workflow. GP, ML model and dermatologist/s assessments will be compared to calculate the precision, sensitivity, specificity and accuracy of the ML model.

Conditions

Skin Diseases

Keywords

Machine Learning; Artificial Intelligence; Data accuracy; Computed Assisted Diagnosis; Neural Network Computer

Outcome Measures

Primary Outcomes (4)

• Sensitivity of the ML model

  True positive rate of the ML model

  1 year

• Specificity of the ML model

  True negative rate of the ML model

  1 year

• Accuracy of the ML model

  Ratio of number of correct predictions to the total number of input samples

  1 year

• Area under the receiver operating characteristic curve of the ML model

  Diagnostic ability of the ML model

  1 year

Secondary Outcomes (1)

• Rate of the eligible participants who agree to participate in the study

  1 year

Study Arms (1)

Diagnostic Test: ML model

EXPERIMENTAL

The diagnostic capacity of the ML model will be compared with that of the general practitioners and with dermatologist.

Diagnostic Test: Autoderm® dermatology search engine (ML model) testing

Interventions

Autoderm® dermatology search engine (ML model) testing DIAGNOSTIC_TEST

GP using a smartphone camera will take an image of the skin problem and will use the Autoderm ML interface to upload the anonymized image. The obtained predicted diagnosis will be recorded in case report form.

Diagnostic Test: ML model

Eligibility Criteria

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

You may qualify if:

• Patients who have cutaneous disease reason-for-visit.
• Patients who provide written informed consent.
• Patients who are 18 years of age or older.

You may not qualify if:

• Patients with advanced dementia.
• Patients with a cutaneous lesion which can't be photographed with a smartphone and images with poor quality.
• Patients who have conditions associated with risk of poor protocol compliance.

Sponsors & Collaborators

• Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina: lead
• iDoc24: collaborator
• Institut Català de la Salut: collaborator

Study Sites (1)

CAP Navàs

Navàs, Barcelona, 08670, Spain

Location

Related Publications (16)

• Lim HW, Collins SAB, Resneck JS Jr, Bolognia JL, Hodge JA, Rohrer TA, Van Beek MJ, Margolis DJ, Sober AJ, Weinstock MA, Nerenz DR, Smith Begolka W, Moyano JV. The burden of skin disease in the United States. J Am Acad Dermatol. 2017 May;76(5):958-972.e2. doi: 10.1016/j.jaad.2016.12.043. Epub 2017 Mar 1.

  PMID: 28259441 RESULT

• Schofield JK, Fleming D, Grindlay D, Williams H. Skin conditions are the commonest new reason people present to general practitioners in England and Wales. Br J Dermatol. 2011 Nov;165(5):1044-50. doi: 10.1111/j.1365-2133.2011.10464.x. Epub 2011 Sep 22.

  PMID: 21692764 RESULT

• Dokotor.se [Internet]. Survey Telemedicine statistics Dokotor.se, the % of queries that are dermatology related 2019 [cited 2019]

  RESULT

• Activitat assistencial de la xarxa sanitària de Catalunya, any 2012: registre del conjunt mínim bàsic de dades (CMBD). Barcelona: Departament de Salut. 2013.

  RESULT

• Lowell BA, Froelich CW, Federman DG, Kirsner RS. Dermatology in primary care: Prevalence and patient disposition. J Am Acad Dermatol. 2001 Aug;45(2):250-5. doi: 10.1067/mjd.2001.114598.

  PMID: 11464187 RESULT

• Porta N, San Juan J, Grasa MP, Simal E, Ara M, Querol MA. [Diagnostic agreement between primary care physicians and dermatologists in the health area of a referral hospital]. Actas Dermosifiliogr. 2008 Apr;99(3):207-12. Spanish.

  PMID: 18358196 RESULT

• Lopez Segui F, Franch Parella J, Girones Garcia X, Mendioroz Pena J, Garcia Cuyas F, Adroher Mas C, Garcia-Altes A, Vidal-Alaball J. A Cost-Minimization Analysis of a Medical Record-based, Store and Forward and Provider-to-provider Telemedicine Compared to Usual Care in Catalonia: More Agile and Efficient, Especially for Users. Int J Environ Res Public Health. 2020 Mar 18;17(6):2008. doi: 10.3390/ijerph17062008.

  PMID: 32197434 RESULT

• Borve A, Dahlen Gyllencreutz J, Terstappen K, Johansson Backman E, Aldenbratt A, Danielsson M, Gillstedt M, Sandberg C, Paoli J. Smartphone teledermoscopy referrals: a novel process for improved triage of skin cancer patients. Acta Derm Venereol. 2015 Feb;95(2):186-90. doi: 10.2340/00015555-1906.

  PMID: 24923283 RESULT

• Ferrer RT, Bezares AP, Manes AL, Mas AV, Gutierrez IT, Llado CN, Estaras GM. [Diagnostic reliability of an asynchronous teledermatology consultation]. Aten Primaria. 2009 Oct;41(10):552-7. doi: 10.1016/j.aprim.2008.11.012. Epub 2009 Jun 5. Spanish.

  PMID: 19500880 RESULT

• Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial Intelligence Applications in Dermatology: Where Do We Stand? Front Med (Lausanne). 2020 Mar 31;7:100. doi: 10.3389/fmed.2020.00100. eCollection 2020.

  PMID: 32296706 RESULT

• Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25.

  PMID: 28117445 RESULT

• 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 RESULT

• Kamulegeya L, Bwanika J, Okello M, Rusoke D, Nassiwa F, Lubega W, Musinguzi D, Borve A. Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning. Afr Health Sci. 2023 Jun;23(2):753-763. doi: 10.4314/ahs.v23i2.86.

  PMID: 38223594 RESULT

• Evaluation of the diagnostic accuracy of an online artificial intelligence app for skin disease diagnosis. Alexander Larson, Degree Project in Medicine, Sahlgrenska University Hospital Department of Dermatology and Venereology, Gothenburg, Sweden 2018.

  RESULT

• Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Holland-Letz T, Utikal JS, von Kalle C; Collaborators. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019 May;113:47-54. doi: 10.1016/j.ejca.2019.04.001. Epub 2019 Apr 10.

  PMID: 30981091 RESULT

• Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, Kalloo A, Hassen ABH, Thomas L, Enk A, Uhlmann L; Reader study level-I and level-II Groups; Alt C, Arenbergerova M, Bakos R, Baltzer A, Bertlich I, Blum A, Bokor-Billmann T, Bowling J, Braghiroli N, Braun R, Buder-Bakhaya K, Buhl T, Cabo H, Cabrijan L, Cevic N, Classen A, Deltgen D, Fink C, Georgieva I, Hakim-Meibodi LE, Hanner S, Hartmann F, Hartmann J, Haus G, Hoxha E, Karls R, Koga H, Kreusch J, Lallas A, Majenka P, Marghoob A, Massone C, Mekokishvili L, Mestel D, Meyer V, Neuberger A, Nielsen K, Oliviero M, Pampena R, Paoli J, Pawlik E, Rao B, Rendon A, Russo T, Sadek A, Samhaber K, Schneiderbauer R, Schweizer A, Toberer F, Trennheuser L, Vlahova L, Wald A, Winkler J, Wolbing P, Zalaudek I. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018 Aug 1;29(8):1836-1842. doi: 10.1093/annonc/mdy166.

  PMID: 29846502 RESULT

MeSH Terms

Conditions

Skin Diseases

Condition Hierarchy (Ancestors)

Skin and Connective Tissue Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 28, 2020
• First Posted: September 24, 2020
• Study Start: January 15, 2021
• Primary Completion: December 31, 2021
• Study Completion: December 31, 2021
• Last Updated: May 5, 2022

Record last verified: 2022-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, CSR
• Time Frame: End of the study
• Access Criteria: Information will be published in international scientific journals

Locations

ES (1)