<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nothnagel, Kerstin</style></author><author><style face="normal" font="default" size="100%">Aslam, Mohammed Farid</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard ultrasound: a feasibility study</style></title><secondary-title><style face="normal" font="default" size="100%">BJGP Open</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024-12-01 00:00:00</style></date></pub-dates></dates><elocation-id><style  face="normal" font="default" size="100%">BJGPO.2024.0057</style></elocation-id><doi><style  face="normal" font="default" size="100%">10.3399/BJGPO.2024.0057</style></doi><volume><style face="normal" font="default" size="100%">8</style></volume><issue><style face="normal" font="default" size="100%">4</style></issue><abstract><style  face="normal" font="default" size="100%">Background This study evaluates the feasibility of remote deep venous thrombosis (DVT) diagnosis via ultrasound sequences facilitated by ThinkSono Guidance, an artificial intelligence (AI) app for point-of-care ultrasound (POCUS).Aim To assess the effectiveness of AI-guided POCUS conducted by non-specialists in capturing valid ultrasound images for remote diagnosis of DVT.Design &amp; setting Over a 3.5-month period, patients with suspected DVT underwent AI-guided POCUS conducted by non-specialists using a handheld ultrasound probe connected to the app. These ultrasound sequences were uploaded to a cloud dashboard for remote specialist review. Additionally, participants received formal DVT scans.Method Patients underwent AI-guided POCUS using handheld probes connected to the AI app, followed by formal DVT scans. Ultrasound sequences acquired during the AI-guided scan were uploaded to a cloud dashboard for remote specialist review, where image quality was assessed, and diagnoses were provided.Results Among 91 predominantly older female participants, 18% of scans were incomplete. Of the rest, 91% had sufficient quality, with 64% categorised by remote clinicians as 'compressible' or 'incompressible'. Sensitivity and specificity for adequately imaged scans were 100% and 91%, respectively. Notably, 53% were low risk, potentially obviating formal scans.Conclusion ThinkSono Guidance effectively directed non-specialists, streamlining DVT diagnosis and treatment. It may reduce the need for formal scans, particularly with negative findings, and extend diagnostic capabilities to primary care. The study highlights AI-assisted POCUS potential in improving DVT assessment.</style></abstract></record></records></xml>