Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and meta-analysis
Notice bibliographique
Résumé
Background and AimsThe adenoma detection rate is a key quality metric for colonoscopy and is inversely related to the post-colonoscopy colorectal cancer rate. Natural language processing can be used to automate the generation of such quality metrics from colonoscopy reports. We performed a systematic review and meta-analysis on the performance of natural language processing (NLP) in identifying adenoma detection in colonoscopy and paired pathology reports.MethodsWe performed a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. A literature query was conducted on MEDLINE, Embase, and Cochrane Database of Systematic Reviews through July 2022. Studies were included if they reported on the operator characteristics of an NLP algorithm in interpreting adenoma detection in colonoscopy and pathology reports. Two authors independently screened studies and abstracted data using an a priori designed data collection form. Performance characteristics were pooled by first using a univariate analysis, followed by a bivariate analysis of sensitivity and specificity.ResultsThe pooled specificity and sensitivity for identifying adenoma detection were .997 (95% confidence interval [CI], .984-.999) and .978 (95% CI, .938-.992). The pooled positive predictive value, negative predictive value, and F1 score were .997 (95% CI, .979-1.00), .977 (95% CI, .938-.992), and .982 (95% CI, .957-.993), respectively. In the bivariate analysis, the pooled specificity and sensitivity were .992 (95% CI, .978-.997) and .973 (95% CI, .929-.990). The NLP systems performed similarly well in identifying the detection of sessile serrated lesions and advanced adenomas.ConclusionsNLP systems can identify adenoma detection from colonoscopy and pathology reports with strong operator characteristics. The adenoma detection rate is a key quality metric for colonoscopy and is inversely related to the post-colonoscopy colorectal cancer rate. Natural language processing can be used to automate the generation of such quality metrics from colonoscopy reports. We performed a systematic review and meta-analysis on the performance of natural language processing (NLP) in identifying adenoma detection in colonoscopy and paired pathology reports. We performed a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. A literature query was conducted on MEDLINE, Embase, and Cochrane Database of Systematic Reviews through July 2022. Studies were included if they reported on the operator characteristics of an NLP algorithm in interpreting adenoma detection in colonoscopy and pathology reports. Two authors independently screened studies and abstracted data using an a priori designed data collection form. Performance characteristics were pooled by first using a univariate analysis, followed by a bivariate analysis of sensitivity and specificity. The pooled specificity and sensitivity for identifying adenoma detection were .997 (95% confidence interval [CI], .984-.999) and .978 (95% CI, .938-.992). The pooled positive predictive value, negative predictive value, and F1 score were .997 (95% CI, .979-1.00), .977 (95% CI, .938-.992), and .982 (95% CI, .957-.993), respectively. In the bivariate analysis, the pooled specificity and sensitivity were .992 (95% CI, .978-.997) and .973 (95% CI, .929-.990). The NLP systems performed similarly well in identifying the detection of sessile serrated lesions and advanced adenomas. NLP systems can identify adenoma detection from colonoscopy and pathology reports with strong operator characteristics.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,001 |
| Bibliométrie | 0,000 | 0,002 |
| 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 ».