AIMC Topic: Thrombectomy

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Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy.

Scientific reports
Acute ischemic stroke (AIS) is a leading global cause of mortality and morbidity. Improving long-term outcome predictions after thrombectomy can enhance treatment quality by supporting clinical decision-making. With the advent of interpretable deep l...

Assessing the clinical reasoning of ChatGPT for mechanical thrombectomy in patients with stroke.

Journal of neurointerventional surgery
BACKGROUND: Artificial intelligence (AI) has become a promising tool in medicine. ChatGPT, a large language model AI Chatbot, shows promise in supporting clinical practice. We assess the potential of ChatGPT as a clinical reasoning tool for mechanica...

Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

Stroke
BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models u...

Cephalic inferior vena cava non-clamping technique versus standard procedure for robot-assisted laparoscopic level II-III thrombectomy: a prospective cohort study.

International journal of surgery (London, England)
BACKGROUND: Renal tumour can invade the venous system and ~4-10% patients with renal tumour had venous thrombus. Although the feasibility of robot-assisted laparoscopic inferior vena cava thrombectomy (RAL-IVCT) in patients with inferior vena cava (I...

Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy.

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
BackgroundIntra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological ...

Deep Learning-based Assessment of Internal Carotid Artery Anatomy to Predict Difficult Intracranial Access in Endovascular Recanalization of Acute Ischemic Stroke.

Clinical neuroradiology
BACKGROUND: Endovascular thrombectomy (EVT) duration is an important predictor for neurological outcome. Recently it was shown that an angle of ≤ 90° of the internal carotid artery (ICA) is predictive for longer EVT duration. As manual angle measurem...

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.

European radiology
OBJECTIVES: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a mode...

Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: Prediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO.