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1.
Tech Coloproctol ; 27(12): 1219-1225, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-37036637

RESUMO

PURPOSE: When an optical colonoscopy is carried out, Scope Guide can assist the endoscopist in determining the localization. In colon capsule endoscopy (CCE), this support is not available. To our knowledge, the interobserver agreement on landmark identification has never been studied. This study aims to investigate the interobserver agreement on landmark identification in CCE. METHODS: An interobserver study was carried out comparing the landmark identification (the ileocecal valve, hepatic flexure, splenic flexure, and anus) in CCE investigations between an external private contractor and three in-house CCE readers with different levels of experience. All CCE investigations analyzed in this study were carried out as a part of the Danish screening program for colorectal cancer. Patients were between 50 and 74 years old with a positive fecal immunochemical test (FIT). A random sample of 20 CCE investigations was taken from the total sample of more than 800 videos. RESULTS: Overall interobserver agreement on all landmarks was 51%. Interobserver agreement on the first cecal image (ileocecal valve), hepatic flexure, splenic flexure, and last rectal image (anus) was 72%, 29%, 22%, and 83%, respectively. The overall interobserver agreement, including only examinations with adequate bowel preparation (n = 16), was 54%, and for individual landmarks, 73%, 32%, 24%, and 85%. CONCLUSION: Overall interobserver agreement on all four landmarks from CCE was poor. Measures are needed to improve landmark identification in CCE investigations. Artificial intelligence could be a possible solution to this problem.


Assuntos
Endoscopia por Cápsula , Neoplasias Colorretais , Humanos , Pessoa de Meia-Idade , Idoso , Variações Dependentes do Observador , Inteligência Artificial , Neoplasias Colorretais/diagnóstico por imagem , Estudos Prospectivos , Colonoscopia/métodos
2.
United European Gastroenterol J ; 8(7): 782-789, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32731841

RESUMO

BACKGROUND: Guidelines suggest computed tomography colonography (CTC) following incomplete optical colonoscopy (OC). Colon capsule endoscopies (CCE) have been suggested as an alternative, although completion rates have been unsatisfactory. Introduction of artificial intelligence (AI)-based localization algorithms of the camera capsules may enable identification of incomplete CCE investigations overlapping with incomplete OCs. OBJECTIVE: The study aims to investigate relative sensitivity of CCE compared with CTC following incomplete OC, investigate the completion rate when combining results from the incomplete OC and CCE, and develop a forward-tracking algorithm ensuring a safe completeness of combined investigations. METHODS: In this prospective paired study, patients with indication for CTC following incomplete OC were included for CCE and CTC. Location of CCE abortion and OC abortion were registered to identify complete combined investigations. AI-based algorithm for localization of capsules were developed reconstructing the passage of the colon. RESULTS: In 237 individuals with CTC indication; 105 were included, of which 97 underwent both a CCE and CTC. CCE was complete in 66 (68%). Including CCEs which reached most oral point of incomplete OC, 73 (75%) had complete colonic investigations; 78 (80%) had conclusive investigations. Relative sensitivity of CCE compared with CTC was 2.67 (95% confidence interval (CI) 1.76;4.04) for polyps >5 mm and 1.91 (95% CI 1.18;3.09) for polyps >9 mm. An AI-based algorithm was developed. CONCLUSION: Sensitivity of CCE following incomplete OC was superior to CTC. Introducing and improving algorithm-based localization of capsule abortion may increase identification of overall complete investigation rates following incomplete OC.ClinicalTrials.gov identifier: NCT02826993.


Assuntos
Inteligência Artificial , Endoscopia por Cápsula/estatística & dados numéricos , Pólipos do Colo/diagnóstico , Colonografia Tomográfica Computadorizada/estatística & dados numéricos , Neoplasias Colorretais/diagnóstico , Processamento de Imagem Assistida por Computador/métodos , Idoso , Endoscopia por Cápsula/métodos , Colo/diagnóstico por imagem , Colo/patologia , Pólipos do Colo/patologia , Colonoscopia/métodos , Colonoscopia/estatística & dados numéricos , Neoplasias Colorretais/patologia , Detecção Precoce de Câncer , Feminino , Humanos , Mucosa Intestinal/diagnóstico por imagem , Mucosa Intestinal/patologia , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Sensibilidade e Especificidade
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