Orthopedics

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biom...

A Review on the Current Applications of Artificial Intelligence in the Operating Room.

. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. . PubMed, Embase, Cochrane Library, and IEEE X...

Value of 3D preoperative planning for primary total hip arthroplasty based on artificial intelligence technology.

BACKGROUND: Accurate preoperative planning is an important step for accurate reconstruction in total hip arthroplasty (THA). Presently, preoperative p...

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation.

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the ...

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study.

OBJECTIVE: To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different...

Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network.

Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structure...

Learning curve for active robotic total knee arthroplasty.

PURPOSE: Total Knee Arthroplasty (TKA) procedures incorporate technology in an attempt to improve outcomes. The Active Robot (ARo) performs a TKA with...

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, th...

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images.

SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classificati...

Recurrent neural network with noise rejection for cyclic motion generation of robotic manipulators.

Recurrent neural network (RNN), as a kind of neural network with outstanding computing capability, improvability, and hardware realizability, has been...

Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery.

In this work, we developed and validated a computer method capable of robustly detecting drill breakthrough events and show the potential of deep lear...

A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty.

BACKGROUND: Inappropriate acetabular component angular position is believed to increase the risk of hip dislocation after total hip arthroplasty. Howe...

Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports.

PURPOSE: The purpose of this study is to investigate relationship of patient age and sex to patterns of degenerative spinal stenosis on lumbar MRI (LM...

Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs.

BACKGROUND: Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. T...

A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.

Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma s...

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning.

We present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To rem...

Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.

Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (di...

Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning.

PURPOSE: We assessed the ability of deep learning (DL) models to distinguish between tear meniscus of lacrimal duct obstruction (LDO) patients and nor...

Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use.

To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of ...

Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study.

PURPOSE: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence ...