首页|Weifang Medical University Reports Findings in Cerebral Hemorrhage (Machine lear ning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis)

Weifang Medical University Reports Findings in Cerebral Hemorrhage (Machine lear ning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Cerebral Hemorrhage is the subject of a report. Accor ding to news reporting out of Weifang, People's Republic of China, by NewsRx edi tors, research stated, "Early identification of hematoma enlargement and persist ent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinic ally effective tools, radiomics has been gradually introduced into the early ide ntification of hematoma enlargement." Our news journalists obtained a quote from the research from Weifang Medical Uni versity, "Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to ex plore the value of radiomics in the early detection of HE in patients with cereb ral hemorrhage. Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles a re considered eligible. The radiomics quality scoring (RQS) tool was used to eva luate included studies. A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical featur es. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical mo dels (0.69 C-index in the training cohort and 0.70 C-index in the validation coh ort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts."

WeifangPeople's Republic of ChinaAsi aCentral Nervous System Diseases and ConditionsCerebral HemorrhageCyborgsEmerging TechnologiesHealth and MedicineHematomaMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.21)