AIMC Topic: Medical Futility

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Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.

The Journal of surgical research Apr 26, 2025
INTRODUCTION: About 25% of patients undergoing pancreaticoduodenectomy (PD) for right-sided pancreatic ductal adenocarcinoma (PDAC) die within 1 y of diagnosis. These patients carry all the risks of significant morbidity with no survival advantage wh...
Female Aged Male Machine Learning Carcinoma, Pancreatic Ductal Retrospective Studies Humans Pancreaticoduodenectomy Medical Futility Middle Aged Risk Assessment Pancreatic Neoplasms
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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.

Stroke and vascular neurology Dec 30, 2024
BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning met...
Machine Learning Decision Support Techniques Databases, Factual Humans Reproducibility of Results Risk Factors Aged Prospective Studies Clinical Decision-Making Female Treatment Outcome Predictive Value of Tests Ischemic Stroke Endovascular Procedures Middle Aged Medical Futility Risk Assessment Time Factors Aged, 80 and over Male
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Upfront surgery for intrahepatic cholangiocarcinoma: Prediction of futility using artificial intelligence.

Surgery Sep 24, 2024
OBJECTIVE: We sought to identify patients at risk of "futile" surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)-based model based on preoperative variables.
Medical Futility Deep Learning ROC Curve Neoplasm Recurrence, Local Hepatectomy Bile Duct Neoplasms Cholangiocarcinoma Male Middle Aged Risk Assessment Female Aged Humans Artificial Intelligence Retrospective Studies
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Ensemble machine learning to predict futile recanalization after mechanical thrombectomy based on non-contrast CT imaging.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Aug 6, 2024
OBJECTIVES: Despite successful recanalization after Mechanical Thrombectomy (MT), approximately 25 % of patients with Acute Ischemic Stroke (AIS) due to Large Vessel Occlusion (LVO) show unfavorable clinical outcomes, namely Futile Recanalization (FR...
Risk Factors Aged Aged, 80 and over Male Middle Aged Risk Assessment Infarction, Middle Cerebral Artery Humans Thrombectomy Tomography, X-Ray Computed Machine Learning Decision Support Techniques Medical Futility Clinical Decision-Making Predictive Value of Tests Retrospective Studies Female Ischemic Stroke Time Factors Radiographic Image Interpretation, Computer-Assisted Treatment Outcome
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Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

BMC medical imaging Jul 19, 2024
OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular tre...
Radiomics Humans Medical Futility Male Machine Learning Female Prognosis Middle Aged Nomograms Aged Retrospective Studies Ischemic Stroke Tomography, X-Ray Computed Endovascular Procedures
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Commentary on Jecker.

Journal of medical ethics Nov 24, 2020
Medical Futility Persons with Disabilities Humans Decision Making Aged Robotics
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