AIMC Topic: Middle Aged

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Predicting the Healing of Lower Extremity Fractures Using Wearable Ground Reaction Force Sensors and Machine Learning.

Sensors (Basel, Switzerland)
Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture h...

Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Acta neuropathologica communications
Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveragi...

[Prioritized appointment allocation in new patients, what is really decisive? : Comparative analysis of manual appointment allocation with automated and AI-assisted approaches].

Zeitschrift fur Rheumatologie
BACKGROUND: The timely allocation of appointments for new patients is a daily challenge in rheumatological practice, which can be supported by digital solutions. The question is to find the simplest and most effective possible method for prioritizati...

Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty.

Journal of shoulder and elbow surgery
BACKGROUND: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and ...

Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients.

Surgical innovation
BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use mac...

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotempo...

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma.

Neuroscience
Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the explo...

Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.

Science bulletin
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures p...

Multi-class segmentation of temporomandibular joint using ensemble deep learning.

Scientific reports
Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the...

Prediction of sepsis mortality in ICU patients using machine learning methods.

BMC medical informatics and decision making
PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have utilized machine learning for prog...