AIMC Topic: Female

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Deep learning for automated hip fracture detection and classification : achieving superior accuracy.

The bone & joint journal
AIMS: The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy.

Development and validation of an interpretable machine learning model to predict major adverse cardiovascular events after noncardiac surgery in geriatric patients: a prospective study.

International journal of surgery (London, England)
BACKGROUND: Major adverse cardiovascular events (MACEs) within 30 days following noncardiac surgery are prognostically relevant. Accurate prediction of risk and modifiable risk factors for postoperative MACEs is critical for surgical planning and pat...

Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.

International journal of surgery (London, England)
BACKGROUND: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to d...

Machine learning-based risk prediction of mild cognitive impairment in patients with chronic heart failure: A model development and validation study.

Geriatric nursing (New York, N.Y.)
Accurate identification of individuals at high risk for mild cognitive impairment (MCI) among chronic heart failure (CHF) patients is crucial for reducing rehospitalization and mortality rates. This study aimed to develop and validate a machine learn...

Automated measurement of pelvic parameters using convolutional neural network in complex spinal deformities: overcoming challenges in coronal deformity cases.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Accurate and consistent measurement of sagittal alignment is challenging, particularly in patients with severe coronal deformities, including degenerative lumbar scoliosis (DLS).

Statin use and longitudinal bone marrow lesion burden: analysis of knees without osteoarthritis from the Osteoarthritis Initiative study.

Skeletal radiology
OBJECTIVES: Knee subchondral bone marrow lesions (BMLs) are one of the hallmark features of structural osteoarthritis (OA) and are potential targets for statins' disease-modifying effect. We aimed to determine the association between statin use and l...

Development of a machine learning model and a web application for predicting neurological outcome at hospital discharge in spinal cord injury patients.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Spinal cord injury (SCI) is a devastating condition with profound physical, psychological, and socioeconomic consequences. Despite advances in SCI treatment, accurately predicting functional recovery remains a significant challenge. Conve...

External validation of 12 existing survival prediction models for patients with spinal metastases.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making.

The perception and use of generative AI for science-related information search: Insights from a cross-national study.

Public understanding of science (Bristol, England)
Publicly accessible large language models like ChatGPT are emerging as novel information intermediaries, enabling easy access to a wide range of science-related information. This study presents survey data from seven countries ( = 4320) obtained in J...

Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.

Journal of the World federation of orthodontists
INTRODUCTION: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment o...