A125 DOES AI INFLUENCE ADENOMA DETECTION RATES IN FIT-POSITIVE PATIENTS
Notice bibliographique
Résumé
Abstract Background The Fecal Immunochemical Test (FIT) is a screening tool that identifies patients more likely to harbor adenomas or colorectal cancer (CRC). Rates of adenoma detection during colonoscopy can vary significantly among endoscopists. Artificial Intelligence (AI) has been shown to improve adenoma detection during colonoscopy. Aims To assess if AI assistance during colonoscopy can improve adenoma detection in FIT-positive patients. Methods In October and November of 2023, AI was utilized during colonoscopy for FIT-positive patients at Eastern Health, Newfoundland. The 61 FIT-positive patients who had colonoscopy with AI assistance were compared to 61 FIT-positive age, gender, and endoscopist-matched controls who had colonoscopies performed without AI assistance during the preceding 6 months. Demographic data were collected on all patients as well as colonoscopy findings, including CRC detection, polyp detection, and histology. The primary outcome was the proportion of patients with adenomas in the two groups. Secondary outcomes included advanced adenoma detection rate and sessile serrated lesion detection rate. Sample size was one of convenience as only 61 FIT-positive patients had colonoscopies with AI assistance while AI was temporarily available at our institution. Data were entered in SPSS version 17 for analysis. A chi-squared test was used to compare proportions. The study received approval from the local Health Research Ethics Board. Results 122 colonoscopies performed by 13 endoscopists (7 General Surgeons, 6 Gastroenterologists) were included. Sixty-eight patients (55.7%) were female and fifty-four patients (44.3%) were male with an average age of 64 (SD = 6.729). Of the patients who underwent AI-assisted colonoscopies, 73.8% had adenomas. Of the patients who underwent colonoscopy without AI assistance, 63.9% had adenomas. There was a non-significant trend toward higher adenoma detection in the patients who had a colonoscopy with AI (x^2=5.165, p=0.076). AI-assisted colonoscopies found advanced adenomas in 44.3% of patients compared to 29.5% in the no AI group (x^2=3,020, p=0.221). In the AI group, 8.2% of patients had sessile serrated lesions (SSL), compared to 1.6% in the standard procedure group (x^2=2.805, p=0.094). Conclusions This study showed a trend toward higher adenoma, advanced adenoma, and SSL detection with AI assistance in FIT-positive patients. Repeating this study with a larger sample size might clarify the effect of AI assistance during colonoscopy in FIT-positive patients Funding Agencies None
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».