首页|Free University of Brussels (ULB) Reports Findings in Artificial Intelligence (R adiographic Detection of Post-Traumatic Bone Fractures: Contribution of Artifici al Intelligence Software to the Analysis of Senior and Junior Radiologists)

Free University of Brussels (ULB) Reports Findings in Artificial Intelligence (R adiographic Detection of Post-Traumatic Bone Fractures: Contribution of Artifici al Intelligence Software to the Analysis of Senior and Junior Radiologists)

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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 from Brussels, Belgium , by NewsRx journalists, research stated, “The aims of this study were: (a) to e valuate the performance of an artificial intelligence (AI) software package (Bon eview Trauma, Gleamer) for the detection of post-traumatic bone fractures in rad iography as a standalone; (b) used by two radiologists (osteoarticular senior an d junior); and ©to determine to whom AI would be most helpful. Within 14 days o f a trauma, 101 consecutive patients underwent radiographic examination of the u pper or lower limbs.” The news correspondents obtained a quote from the research from the Free Univers ity of Brussels (ULB), “The definite diagnosis for identifying fractures was: (a ) radio-clinical consensus between the radiologist on-call who analyzed the imag es and the orthopedist (Group 1); (b) Cone Beam computed tomography (CBCT) explo ration of the area of interest, in case of doubts or absence of consensus (Group 2). Independently of this diagnosis for both groups, the radiographic images we re separately analyzed by two radiologists (osteoarticular senior: SR; junior: J R) prior without, and thereafter with the results of AI. AI performed better tha n the radiologists in detecting common fractures (Group 1), but not subtle fract ures (Group 2). In association with AI, both radiologists increased their overal l performances in both groups, whereas this increase was significantly higher fo r the JR (<0.05). AI is reliable for common radiographic f racture identification and is a useful learning tool for radiologists in trainin g.”

Brussels, Belgium, Europe, Artificial In telligence, Bone Diseases and Conditions, Bone Fractures, Bone Research, Emergin g Technologies, Health and Medicine, Machine Learning, Software

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN：
年,卷(期)：2024.(MAY.9)