首页|University of Bristol Reports Findings in Deep Vein Thrombosis (Evaluating the b enefits of machine learning for diagnosing deep vein thrombosis compared to gold standard ultrasound- a feasibility study)
University of Bristol Reports Findings in Deep Vein Thrombosis (Evaluating the b enefits of machine learning for diagnosing deep vein thrombosis compared to gold standard ultrasound- a feasibility study)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cardiovascular Disease s and Conditions - Deep Vein Thrombosis is the subject of a report. According to news originating from Bristol, United Kingdom, by NewsRx correspondents, resear ch stated, "This study evaluates the feasibility of remote deep venous thrombosi s (DVT) diagnosis via ultrasound sequences facilitated by ThinkSono Guidance, an artificial intelligence (AI)-app, for point-of-care ultrasound (POCUS). The aim is to assess the effectiveness of AI-guided POCUS conducted by non-specialists in capturing valid ultrasound images for remote diagnosis of DVT." Our news journalists obtained a quote from the research from the University of B ristol, "Over a 3.5- month period, patients with suspected DVT underwent AI-guide d POCUS conducted by non-specialists using a handheld ultrasound probe connected to the app. These ultrasound sequences were uploaded to a cloud-dashboard for r emote specialist review. Additionally, participants received a formal DVT scans. Patients underwent AI-guided POCUS using handheld probes connected to the AI-ap p, followed by formal DVT scans. Ultrasound sequences acquired during the AI-gui ded scan were uploaded to a cloud-dashboard for remote specialist review, where image quality was assessed, and diagnoses were provided. Among 91 predominantly elderly female participants, 18% of scans were incomplete. Of the rest, 91% had sufficient quality, with 64% categoris ed by remote clinicians as 'compressible' or 'incompressible.' Sensitivity and s pecificity for adequately imaged scans were 100% and 91% , respectively. Notably, 53% were low risk, potentially obviating formal scans. ThinkSono Guidance effectively directed non-specialists, streamlin ing DVT diagnosis and treatment. It may reduce the need for formal scans, partic ularly with negative findings, and extend diagnostic capabilities to primary car e."
BristolUnited KingdomEuropeCardiov ascular Diseases and ConditionsCyborgsDeep Vein ThrombosisDiagnostics and ScreeningEmbolism and ThrombosisEmerging TechnologiesHealth and MedicineHematologyMachine LearningThrombosisVascular Diseases and Conditions
NETL
NSTL
