A32 ARTIFICIAL INTELLIGENCE USE IN DIAGNOSIS & MONITORING OF INFLAMMATORY BOWEL DISEASE: A SCOPING REVIEW
Notice bibliographique
Résumé
Abstract Background Inflammatory bowel diseases (IBD) are a family of immune-mediated conditions, which are increasing in incidence and prevalence worldwide. Assessment of IBD is done through endoscopy, video capsule endoscopy (VCE), histology, and various imaging modalities including ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Considering the increasing complexities in the assessment of IBD, artificial intelligence (AI) is an important adjunct with potential to enhance diagnosis, drug response and prediction of disease course Aims We conducted a scoping review to assess AI in diagnosis, monitoring, and prognostication of patients with IBD, to aid in identification of gaps in knowledge to guide future research endeavors. Methods The scoping review protocol was adapted from the recommendations laid out by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Scoping Review Extension (PRISMA-ScR). Electronic databases used in the literature search included MEDLINE, EMBASE, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Engineering Village. Two reviewers independently screened the abstracts and titles first before performing full text review. A third review resolved any conflict where needed. All study types were included, and data extraction utilized Covidence. Studies were categorized based on the assessment modality, then themes including, diagnosis, grading activity, prognosis, and monitoring. Results A total of 140 studies were included in the final scoping review. The largest number of studies involved endoscopy at 72 (51%) citations, followed by VCE, histology, MRI, CT, and US at 30 (21%), 18 (13%), 13 (9%), 6 (4%), and 1 (0.7%) citation(s), respectively. When looking at themes, most endoscopy studies examined disease activity (65%) while diagnosis was the most common theme in VCE, MRI and CT (77%, 69% and 83%, respectively). Histologic studies focused on prognosis (89%) and the single US study evaluated both diagnosis and prognosis concomitantly. Amongst all the investigative modalities examined, monitoring of IBD was the least studied theme. Peak performance of AI models for grading disease activity during endoscopy was 98.7% compared to human clinicians with less variability observed. Conclusions With IBD diagnosis and assessment becoming increasingly complex, AI may be a useful adjunctive tool across multiple modalities. Evaluation of use of AI in US is lacking, despite gaining interest in non-invasive assessment of IBD. Further studies are needed incorporating AI use in US while also investigating its role in monitoring IBD disease activity. We hope this scoping review will serve as a future direction for subsequent research in this area. Funding Agencies:
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,009 |
| Bibliométrie | 0,030 | 0,026 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».