Sabina Beg1, Ewa Wronska2, Isis Araujo3, Begona González Suárez3, Ekaterina Ivanova4, Evgeny Fedorov4, Lars Aabakken5, Uwe Seitz6, Jean-Francois Rey7, Jean-Christophe Saurin8, Roberto Tari9, Tim Card10, Krish Ragunath1. 1. NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom; Nottingham Digestive Diseases Centre, The University of Nottingham, Nottingham, United Kingdom. 2. Department of Gastroenterology, Hepatology and Oncology, Center of Postgraduate Medical Education, Warsaw, Poland; Department of Gastroenterological Oncology, Maria Sklodowska-Curie Institute - Oncology Center, Warsaw, Poland. 3. Gastroenterology Department, ICMDiM, Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain. 4. Department of Gastroenterology, Moscow University Hospital N31, Pirogov Russia National Research Medical University, Moscow, Russia. 5. Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway. 6. Department of Gastroenterology, University Hospital Heidelberg, Heppenheim, Germany. 7. Hepato-Gastroenterology Department, Institut Arnault Tzanck, St. Laurent du Var, France. 8. Department of Endoscopy and Gastroenterology, Pavillon L, Edouard Herriot Hospital, Hospices Civils de Lyon, Lyon, France. 9. Gastroenterology Division, Azienda Ospedaliero Universitaria "Maggiore della Carità", Novara, Italy. 10. NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom; Division of Epidemiology and Public Health, School of Medicine The University of Nottingham, Nottingham, United Kingdom.
Abstract
BACKGROUNDS AND AIMS: A typical capsule endoscopy (CE) case generates tens of thousands of images, with abnormalities often confined to a just few frames. Omni Mode is a novel EndoCapsule software algorithm (Olympus, Tokyo, Japan) that proposes to intelligently remove duplicate images while maintaining accuracy in lesion detection. METHODS: This prospective multicenter study took place across 9 European centers. Consecutive, unselected CE cases were read conventionally in normal mode, with every captured frame reviewed. Cases were subsequently anonymized and randomly allocated to another center where they were read using Omni Mode. Detected lesions and reading times were recorded, with findings compared between both viewing modes. The clinical significance of lesions was described according to the P classification (P0, P1, and P2). Where a discrepancy in lesion detection in either mode was found, expert blinded review at a consensus meeting was undertaken. RESULTS: The patient population undergoing CE had a mean age of 49.5 years (range, 18-91), with the investigation of anemia or GI bleeding accounting for 71.8% of cases. The average small-bowel transit time was 4 hours, 26 minutes. The mean reading time in normal mode was 42.5 minutes. The use of Omni Mode was significantly faster (P < .0001), with an average time saving of 24.6 minutes (95% confidence interval, 22.8-26.9). The 2127 lesions were identified and classified according to the P classification as P0 (1234), P1 (656), and P2 (237). Lesions were identified using both reading modes in 40% (n = 936), and 1186 lesions were identified by either normal or Omni Mode alone. Normal mode interpretation was associated with 647 lesions being missed, giving an accuracy of .70. Omni Mode interpretation led to 539 lesions being missed, with an accuracy of .75. There was no significant difference in clinical conclusions made between either reading mode. CONCLUSIONS: This study shows that CE reading times can be reduced by an average of 40%, without any reduction in clinical accuracy.
RCT Entities:
BACKGROUNDS AND AIMS: A typical capsule endoscopy (CE) case generates tens of thousands of images, with abnormalities often confined to a just few frames. Omni Mode is a novel EndoCapsule software algorithm (Olympus, Tokyo, Japan) that proposes to intelligently remove duplicate images while maintaining accuracy in lesion detection. METHODS: This prospective multicenter study took place across 9 European centers. Consecutive, unselected CE cases were read conventionally in normal mode, with every captured frame reviewed. Cases were subsequently anonymized and randomly allocated to another center where they were read using Omni Mode. Detected lesions and reading times were recorded, with findings compared between both viewing modes. The clinical significance of lesions was described according to the P classification (P0, P1, and P2). Where a discrepancy in lesion detection in either mode was found, expert blinded review at a consensus meeting was undertaken. RESULTS: The patient population undergoing CE had a mean age of 49.5 years (range, 18-91), with the investigation of anemia or GI bleeding accounting for 71.8% of cases. The average small-bowel transit time was 4 hours, 26 minutes. The mean reading time in normal mode was 42.5 minutes. The use of Omni Mode was significantly faster (P < .0001), with an average time saving of 24.6 minutes (95% confidence interval, 22.8-26.9). The 2127 lesions were identified and classified according to the P classification as P0 (1234), P1 (656), and P2 (237). Lesions were identified using both reading modes in 40% (n = 936), and 1186 lesions were identified by either normal or Omni Mode alone. Normal mode interpretation was associated with 647 lesions being missed, giving an accuracy of .70. Omni Mode interpretation led to 539 lesions being missed, with an accuracy of .75. There was no significant difference in clinical conclusions made between either reading mode. CONCLUSIONS: This study shows that CE reading times can be reduced by an average of 40%, without any reduction in clinical accuracy.