Orthopedics

A fuzzy-logic approach for longitudinal assessment of patients' psychophysiological state: an application to upper-limb orthopedic robot-aided rehabilitation.

Understanding the psychophysiological state during robot-aided rehabilitation is crucial for assessing the patient experience during treatments. This paper introduces a psychophysiological estimation approach using a Fuzzy Logic inference model to assess patients' perception of robots during upper-limb robot-aided rehabilitation sessions. The patients were asked to perform nine cycles of 3D point-...

Joint suppression of cardiac bSSFP cine banding and flow artifacts using twofold phase-cycling and a dual-encoder neural network.

BACKGROUND: Cardiac balanced steady state free precession (bSSFP) cine imaging suffers from banding and flow artifacts induced by off-resonance. The work aimed to develop a twofold phase cycling sequence with a neural network-based reconstruction (2P-SSFP+Network) for a joint suppression of banding and flow artifacts in cardiac cine imaging.

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine.

PURPOSE: To show the value of custom methods, namely Retrieval Augmented Generation (RAG)-based Large Language Models (LLMs) and Agentic Augmentation,...

A machine-learning-based algorithm for bone marrow cell differential counting.

BACKGROUND: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases....

Utilizing artificial intelligence to determine bone mineral density using spectral CT.

BACKGROUND: Dual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring ded...

Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators.

In this paper, for addressing the safe control problem of modular robot manipulators (MRMs) system with uncertain disturbances, an approximate optimal...

Automated detection of bone lesions using CT and MRI: a systematic review.

PURPOSE: The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, f...

Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.

Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in ass...

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

BACKGROUND: Tubular microdiscectomy (TMD) is a treatment for lumbar disc herniation (LDH). Although the combination of MRI and deep learning (DL) has ...

A critical comparative study of the performance of three AI-assisted programs for bone age determination.

OBJECTIVES: To date, AI-supported programs for bone age (BA) determination for medical use in Europe have almost only been validated separately, accor...

Artificial intelligence tools trained on human-labeled data reflect human biases: a case study in a large clinical consecutive knee osteoarthritis cohort.

Humans have been shown to have biases when reading medical images, raising questions about whether humans are uniform in their disease gradings. Artif...

Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.

Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition,...

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observati...

A machine learning approach for the design optimization of a multiple magnetic and inertial sensors wearable system for the spine mobility assessment.

BACKGROUND: Recently, magnetic and inertial measurement units (MIMU) based systems have been applied in the spine mobility assessment; this evaluation...

Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study.

BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for ...

Artificial intelligence-based analysis of lower limb muscle mass and fatty degeneration in patients with knee osteoarthritis and its correlation with Knee Society Score.

PURPOSE: Lower-limb muscle mass reduction and fatty degeneration develop in patients with knee osteoarthritis (KOA) and could affect their symptoms, s...

HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.

PURPOSE: Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adu...

AFSegNet: few-shot 3D ankle-foot bone segmentation via hierarchical feature distillation and multi-scale attention and fusion.

Accurate segmentation of ankle and foot bones from CT scans is essential for morphological analysis. Ankle and foot bone segmentation challenges due t...

Detection, classification, and characterization of proximal humerus fractures on plain radiographs.

AIMS: The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of grea...

Understanding the role of machine learning in predicting progression of osteoarthritis.

AIMS: Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway toward...