Postoperative Period

Predicting postoperative visual acuity in epiretinal membrane patients and visualization of the contribution of explanatory variables in a machine learning model.

PloS one 39038056

BACKGROUND: The purpose of this study was to develop a model that can predict the postoperative visual acuity in eyes that had undergone vitrectomy for an epiretinal membrane (ERM). The Light Gradient Boosting Machine (LightGBM) was used to evaluate ...

Aged Epiretinal Membrane Female Humans Machine Learning Male Middle Aged Postoperative Period Tomography, Optical Coherence Visual Acuity Vitrectomy

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Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.

Acta ophthalmologica 38682863

PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and ...

Adult Aged Deep Learning Female Fluorescein Angiography Follow-Up Studies Humans Male Middle Aged Postoperative Period Predictive Value of Tests Preoperative Period Recurrence Retinal Detachment Retrospective Studies Scleral Buckling Visual Acuity Vitrectomy

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Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

BMC surgery 38730406

PURPOSE: The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoper...

Adult Aged Defecation Female Humans Machine Learning Male Middle Aged Postoperative Complications Postoperative Period Recovery of Function Retrospective Studies Risk Factors

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[Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation 39155256

OBJECTIVE: Photoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG wit...

Animals Deep Learning Embolism Monitoring, Physiologic Neural Networks, Computer Photoplethysmography Postoperative Period Rabbits Surgical Flaps

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Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

International journal of computer assisted radiology and surgery 39044037

PURPOSE: Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computin...

Adolescent Algorithms Biomechanical Phenomena Computer Simulation Female Finite Element Analysis Humans Imaging, Three-Dimensional Machine Learning Male Postoperative Period Principal Component Analysis Scoliosis Spine

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Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.

Journal of medical Internet research 39197165

BACKGROUND: Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due to risks of recurrence. Such recurrences not only cause physical suffering to the patient but also ad...

Aged Aged, 80 and over Female Hematoma, Subdural, Chronic Humans Machine Learning Male Middle Aged Postoperative Period Radiomics Recurrence Retrospective Studies

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A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.

Journal of translational medicine 39143624

BACKGROUND: Postoperative liver metastasis significantly impacts the prognosis of pancreatic neuroendocrine tumor (panNET) patients after R0 resection. Combining computational pathology and deep learning radiomics can enhance the detection of postope...

Adult Aged Deep Learning Female Humans Liver Neoplasms Male Middle Aged Multivariate Analysis Neuroendocrine Tumors Nomograms Pancreatic Neoplasms Postoperative Period Prognosis Radiomics Tomography, X-Ray Computed

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Development and Internal Validation of Machine Learning to Predict Postoperative Worse Functional Status after Surgical Treatment for Thoracic Spinal Stenosis.

Medical science monitor : international medical journal of experimental and clinical research 39323074

BACKGROUND The objective of this study was to develop and validate machine learning (ML) algorithms to predict the 30-day and 6-month risk of deteriorating functional status following surgical treatment for thoracic spinal stenosis (TSS). We aimed to...

Aged Algorithms Female Humans Machine Learning Male Middle Aged Postoperative Complications Postoperative Period Retrospective Studies Spinal Stenosis Thoracic Vertebrae Treatment Outcome

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Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Journal of magnetic resonance imaging : JMRI 39501646

BACKGROUND: Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has shown promise, its application in evaluating postoperative outcomes in TMD has not been fully explor...

Adult Aged Deep Learning Diskectomy Female Humans Intervertebral Disc Displacement Lumbar Vertebrae Magnetic Resonance Imaging Male Microsurgery Middle Aged Postoperative Period Reproducibility of Results Retrospective Studies Treatment Outcome

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Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.

IEEE transactions on bio-medical engineering 39292577

OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...

Adolescent Brain Child Child, Preschool Connectome Deep Learning Diffusion Tensor Imaging Drug Resistant Epilepsy Female Humans Image Interpretation, Computer-Assisted Language Male Postoperative Period

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Explore the latest research on artificial intelligence and machine learning in medicine.

Predicting postoperative visual acuity in epiretinal membrane patients and visualization of the contribution of explanatory variables in a machine learning model.

Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra-wide field imaging.

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

[Application of Photoplethysmography Combined with Deep Learning in Postoperative Monitoring of Flaps].

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.

A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics.

Development and Internal Validation of Machine Learning to Predict Postoperative Worse Functional Status after Surgical Treatment for Thoracic Spinal Stenosis.

Development of a Dual-Plane MRI-Based Deep Learning Model to Assess the 1-Year Postoperative Outcomes in Lumbar Disc Herniation After Tubular Microdiscectomy.

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.

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