首页|Third Military Medical University Reports Findings in Artificial Intelligence (A new artificial intelligence system for both stomach and small bowel capsule end oscopy)
Third Military Medical University Reports Findings in Artificial Intelligence (A new artificial intelligence system for both stomach and small bowel capsule end oscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Chongqi ng, People's Republic of China, by NewsRx journalists, research stated, "Despite the benefits of artificial intelligence (AI) in small bowel (SB) capsule endosc opy (CE) image reading, information on its application in the stomach and SB CE is lacking. In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning (DL)-based SmartScan (SS), which has been described previously." The news reporters obtained a quote from the research from Third Military Medica l University, "A total of 1,069 magnetically controlled gastrointestinal (GI) CE examinations (comprising 2,672,542 gastric images) were used in the training ph ase for recognizing gastric pathologies, producing a new AI algorithm named SS P lus. 342 fully automated, magnetically controlled CE (FAMCE) examinations were i ncluded in the validation phase. The performance of both senior and junior endos copists with both the SS Plus- Assisted Reading (SSP-AR) and conventional reading (CR) modes was assessed. SS Plus was designed to recognize 5 types of gastric l esions and 17 types of SB lesions. SS Plus reduced the number of CE images requi red for review to 873.90 (1000) (median, IQR 814.50-1,000) versus 44,322.73 (42, 393) (median, IQR 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endosco pists took 9.54 min (8.51) (median, IQR 6.05-13.13) to complete the CE video rea ding. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, w hereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, j unior endoscopists remarkably improved their CE image reading ability with SSP-A R."
ChongqingPeople's Republic of ChinaA siaArtificial IntelligenceEmerging TechnologiesEndoscopyGastroenterologyHealth and MedicineMachine LearningMinimally Invasive Surgical ProceduresSurgery
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