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

May 4, 2022 updated by: Jordi Gol i Gurina Foundation

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

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

A secure anonymous stand alone web interface that is compatible to any mobile device will be integrated with the Autoderm API. The study conducted in this project will consist in a prospective study aimed to evaluate the ML model performance, comparing its diagnostic capacity with GPs and dermatologists.

To conduct the study the following procedure will be executed until the required number of samples is reached:

  1. A suitable patient with skin concern is asked to participate and sign the patient's study agreement.
  2. GP will diagnose the skin condition.
  3. GP (or nurse) will take one good quality image of the skin condition.
  4. GP will send the photograph as a teledermatology consultation following the current workflow.
  5. The image is entered in the Autoderm ML interface.
  6. Dermatologist will diagnose the skin condition.

The study will be conducted in primary care centers managed by the Catalan Health Institute. Participant PCP will be located in rural and metropolitan areas in Central Catalonia, which includes the regions of Anoia, Bages, Moianès, Berguedà and Osona. The reference population included in the study will be about 512,050. The recruitment of prospective subjects will consist on a consecutive basis.

General practitioners will collect data from consecutive patients who meet the inclusion criteria after obtaining written informed consent. Collected data will be reported exclusively in case report form (attached at Annex V and VI).

The GP will diagnose the skin condition and will fill the "Face-to-face assessment by GP". For each patient, the GP using a smartphone camera will take a close up good quality image of the skin problem. The image will be anonymous and it will be not possible to identify patients. The GP will use the Autoderm ML interface to upload the anonymized image and will fill the "Assessment provided by the ML model" questionnaire with the top 3 diagnoses generated by the ML model.

In order to get a second opinion, the GP will incorporate the anonymized image and an accurate description of the skin lesion into the patient's medical history following the current teledermatology flow. The GP will fill "Assessment by teledermatology" questionnaire after receiving the information, being response time about 2-7 days.

In case of dermatology referral, the GP will fill "Assessment by in person dermatologist", by accessing electronic health records as they become available, being the average waiting time for referral from 30 to 90 days.

Questionnaire case number will be the same for all questionnaires and it will not be possible to identify the patient, as case number will be predefined before the initiation of the data collection phase.

To compare the performance of the ML model with that of the GPs and dermatologists, it will be required a sample size of 100 images of skin diseases from patients who meet the inclusion criteria. The proposed sample size is based on sample size calculation used in similar research.

Study Type

Interventional

Enrollment (Actual)

100

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Spain
    • Barcelona
      • Navàs, Barcelona, Spain, 08670
        • CAP Navàs

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

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

Exclusion Criteria:

  • 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: DIAGNOSTIC
  • Allocation: NA
  • Interventional Model: SINGLE_GROUP
  • Masking: NONE

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
EXPERIMENTAL: Diagnostic Test: ML model
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
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity of the ML model
Time Frame: 1 year
True positive rate of the ML model
1 year
Specificity of the ML model
Time Frame: 1 year
True negative rate of the ML model
1 year
Accuracy of the ML model
Time Frame: 1 year
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
Time Frame: 1 year
Diagnostic ability of the ML model
1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Rate of the eligible participants who agree to participate in the study
Time Frame: 1 year
Frequency of patients who agree to participate in the clinical trial and are eligible.
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Jordi Gol i Gurina Foundation

Collaborators

Institut Català de la Salut
iDoc24

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

January 15, 2021

Primary Completion (ACTUAL)

December 31, 2021

Study Completion (ACTUAL)

December 31, 2021

Study Registration Dates

First Submitted

August 28, 2020

First Submitted That Met QC Criteria

September 23, 2020

First Posted (ACTUAL)

September 24, 2020

Study Record Updates

Last Update Posted (ACTUAL)

May 5, 2022

Last Update Submitted That Met QC Criteria

May 4, 2022

Last Verified

March 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • P20/159-P

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

The protocol will be published.

IPD Sharing Time Frame

End of the study

IPD Sharing Access Criteria

Information will be published in international scientific journals

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • CSR

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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