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Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study.

International journal of surgery (London, England)
BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency general surgery (EGS). The American Association for the Surgery of Trauma recommends developing risk assessment tools specifically for EGS-related disease...

Surgical factors play a critical role in predicting functional outcomes using machine learning in robotic-assisted total knee arthroplasty.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: Predictive models help determine predictive factors necessary to improve functional outcomes after total knee arthroplasty (TKA). However, no study has assessed predictive models for functional outcomes after TKA based on the new concepts of...

Prediction of likelihood of conception in dairy cows using milk mid-infrared spectra collected before the first insemination and machine learning algorithms.

Journal of dairy science
Accurate and ex-ante prediction of cows' likelihood of conception (LC) based on milk composition information could improve reproduction management on dairy farms. Milk composition is already routinely measured by mid-infrared (MIR) spectra, which are...

Prediction of surgery-first approach orthognathic surgery using deep learning models.

International journal of oral and maxillofacial surgery
The surgery-first approach (SFA) orthognathic surgery can be beneficial due to reduced overall treatment time and earlier profile improvement. The objective of this study was to utilize deep learning to predict the treatment modality of SFA or the or...

Tricuspid valve flow measurement using a deep learning framework for automated valve-tracking 2D phase contrast.

Magnetic resonance in medicine
PURPOSE: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automa...

Machine learning models based on ultrasound and physical examination for airway assessment.

Revista espanola de anestesiologia y reanimacion
PURPOSE: To demonstrate the utility of machine learning models for predicting difficult airways using clinical and ultrasound parameters.

Validity of facial skin analysis pore detection: A comparative analysis.

Journal of cosmetic dermatology
BACKGROUND: Reliable, objective measures to assess facial characteristics would aid in the assessment of many dermatological treatments. Previous work utilized an iOS application-based artificial intelligence (AI) tool compared to the "gold standard"...

Construction of the prediction model for multiple myeloma based on machine learning.

International journal of laboratory hematology
INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM.

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

Journal of affective disorders
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presenta...

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs).