AIMC Topic: Treatment Outcome

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Detection and Classification of Chronic Total Occlusion lesions using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiovascular disease (CVD) is one of the diseases with the highest mortality rate in modern society, while chronic total occlusion (CTO) is the initial factor that influences the success rate of percutaneous coronary intervention (PCI), which is on...

Artificial Intelligence for the Treatment of Lumbar Spondylolisthesis.

Neurosurgery clinics of North America
Multiple registries are currently collecting patient-specific data on lumbar spondylolisthesis including outcomes data. The collection of imaging diagnostics data along with comparative outcomes data following decompression versus decompression and f...

A Predictive Model for Determining Patients Not Requiring Prolonged Hospital Length of Stay After Elective Primary Total Hip Arthroplasty.

Anesthesia and analgesia
BACKGROUND: Hospital length of stay (LOS) is an important quality metric for total hip arthroplasty. Accurately predicting LOS is important to expectantly manage bed utilization and other hospital resources. We aimed to develop a predictive model for...

[Novel Innovation: Can Artificial Intelligence make Rehabilitation more Efficient?].

Laeknabladid
Demand for Vocational Rehabilitation in Iceland has been steadily rising in recent years where the presence of young patients has increased proportionally the most. It is essential that public spending is efficient without compromising the treatment ...

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

JCO clinical cancer informatics
PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can ...

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis.

Neurosurgical focus
OBJECTIVEPatient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The ...

Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.

Stroke
Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. Thi...

Functional connectivity in multiple sclerosis after robotic rehabilitative treatment: A case report.

Medicine
RATIONALE: Multiple sclerosis (MS) is an inflammatory demyelinating disease of central nervous system and it is associated with an impaired motor function status. The efficacy of rehabilitation in promoting functional recovery and increasing quality ...