A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease

Natural Language Processing and machine learning are examples of artificial intelligence tools. This study will check if these tools correctly identify people with Crohn's disease with complex perianal fistulas from their medical records.

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Key information

Conditions

Crohn Disease Digestive System Diseases Digestive System Fistula Fistula Gastroenteritis Gastrointestinal Diseases Inflammatory Bowel Diseases Intestinal Diseases Intestinal Fistula Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Rectal Diseases Rectal Fistula

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Hospital Universitario Son Espases, Palma, Balearic Islands, Spain

About this study

This is a non-interventional, retrospective study of participants with CD and CPF in a clinical practice setting.

The study will enroll approximately 100 participants.

The study will have a retrospective data collection to select and analyze information from EMRs processed by an AI based analytics framework that uses machine learning and NLP methodologies.

All participants will be enrolled in one observational group.

Participants with CD

This multi-center trial will be conducted in Spain. The overall duration of the study is approximately 36 months.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021.

Exclusion criteria

Not applicable.

Treatment and study plan

Primary outcomes

Percentage of Participants With CD and CPF Accurately Identified With the use of NLP and Medical Language (MEL)

Time frame: Up to Month 36

Percentage of participants will be measured in terms of accuracy and precision (sensitivity and specificity) of the "algorithm" used to identify participants with CPF associated with CD. Data obtained through the artificial intelligence (AI) technology will be compared with data obtained through traditional electronic data capture (EDC) and source data verification methods.

Secondary outcomes

Number of Participants With CD and CPF Characterized Using NLP and Machine Learning Techniques

Time frame: Up to Month 36

The following information at the moment of CPF diagnosis will be extracted from the electronical medical records (EMRs): age, gender, date of diagnosis of CPF, smoking status, date of diagnosis of CD, luminal disease characteristics (localization, behaviour and activity) at diagnosis, treatments (medical and surgical) established for luminal disease in the study period, treatments (medical and surgical) established for CPF since first occurrence, fistula characteristics at diagnosis: type of fistula (following American Gastroenterological Association [AGA] classification) number of fistula internal and external openings, fistula activity.

Sponsors and collaborators

Lead sponsor

Takeda

Industry

Registry information

Official study title

Use of Natural Language Processing (NLP) and Machine Learning (ML) for the Identification of Patients With Crohn's Disease (CD) and Complex Perianal Fistulas (CPF) and Their Characterization in Terms of Clinical and Demographic Characteristics. A Multicentre, Retrospective, NLP Based Study

Acronym: INTUITION-CPF

Important dates

Study start: 2022
Primary completion: 2023
Study completion: 2024
First posted: Apr 14, 2021
Registry last updated: May 10, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed