首页|Nanjing University Reports Findings in Artificial Intelligence [Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL St udy)-a protocol for a ...]
Nanjing University Reports Findings in Artificial Intelligence [Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL St udy)-a protocol for a ...]
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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 Nanjing, People's Republic of China, by NewsRx journalists, research stated, "This multicenter, d ouble-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneury sm detection in CT angiography (CTA) and its influence on patients' short-term a nd long-term outcomes. Prospective, multicenter, double-blinded RCT." Financial supporters for this research include National Natural Science Foundati on of China, Key Programme. The news correspondents obtained a quote from the research from Nanjing Universi ty, "The model was designed for the automatic detection of intracranial aneurysm s from original CTA images. Adult inpatients and outpatients who are scheduled f or head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA int erpreted by radiologists with the assistance of the True-AI-integrated intracran ial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA inter preted by radiologists with the assistance of the Sham-AI-integrated intracrania l aneurysm diagnosis strategy (Sham-AI arm). Block randomization, stratified by center, gender, and age group. Coprimary outcomes of superiority in patient-leve l sensitivity and noninferiority in specificity for the True-AI arm to the Sham- AI arm in intracranial aneurysms. Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis. Study participants and participating radiologists will be blinded to the intervention. Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intrac ranial aneurysms for patients undergoing head CTA in the hospital is approximate ly 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided a = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm. The study will determine the precise impact of the AI s ystem on the detection performance for intracranial aneurysms in a double-blinde d design and following the real-world effects on patients' short-term and long-t erm outcomes. This trial has been registered with the NIH, U.S."
NanjingPeople's Republic of ChinaAsiaAneurysmAngiographyArtificial IntelligenceBrain Diseases and ConditionsCardiologyCardiovascular Diagnostic TechniquesCardiovascular Diseases and ConditionsCentral Nervous System Diseases and ConditionsCerebrovascular Diso rdersClinical ResearchClinical Trials and StudiesDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineIntracranial AneurysmIntracrania l Arterial Diseases and ConditionsMachine LearningVascular Diseases and Cond itions
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