AIMC Topic: Middle Aged

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Predicting inferior vena cava filter complications using machine learning.

Journal of vascular surgery. Venous and lymphatic disorders
OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (M...

Artificial intelligence in predicting postoperative heterotopic ossification following anterior cervical disc replacement.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict high-grade heterotopic ossification (HO) following Anterior cervical disc replacement (ACDR).

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combinin...

A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy.

The ocular surface
PURPOSE: To develop an artificial intelligence (AI) model to diagnose Acanthamoeba keratitis (AK) based on in vivo confocal microscopy (IVCM) images extracted from the Heidelberg Retinal Tomograph 3 (HRT 3).

Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.

Indian journal of ophthalmology
PURPOSE: This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI developme...

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Sensors (Basel, Switzerland)
Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better i...

Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.

Radiation oncology (London, England)
BACKGROUND: In this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa).

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer.

BMC cancer
PURPOSE: A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis...

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.

Scientific reports
The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretabl...

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials.

Journal of neural engineering
Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupli...