Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.

Incomplete tetraplegia, incomplete paraplegia, and cauda equina syndrome are major neurological disorders that significantly reduce patients' quality of life, primarily due to impaired motor function and gait instability. Although conventional neurological assessments and imaging techniques are widely used for diagnosis, they are limited by temporal constraints and physical accessibility. This study explores the integration of machine learning and 3D motion capture gait data for effective classification of these conditions.

How valuable are the questions and answers generated by large language models in oral and maxillofacial surgery?

Introduction: In this study, we aim to evaluate the ability of large language models (LLM) to generate questions and answers in oral and maxillofacial surgery.

Methods: ChatGPT4, ChatGPT4o, and Claude3-Opus were evaluated in this study. Each LLM was instructed to generate 50 questions about oral and maxillofacial surgery.

Deep learning-based classification of speech disorder in stroke and hearing impairment.

Background And Objective: Speech disorders can arise from various causes, including congenital conditions, neurological damage, diseases, and other disorders. Traditionally, medical professionals have used changes in voice to diagnose the underlying causes of these disorders. With the advancement of artificial intelligence (AI), new possibilities have emerged in this field.

AI-Based 3D Liver Segmentation and Volumetric Analysis in Living Donor Data.

This study investigated the application of deep learning for 3-dimensional (3D) liver segmentation and volumetric analysis in living donor liver transplantation. Using abdominal computed tomography data from 55 donors, this study aimed to evaluate the liver segmentation performance of various U-Net-based models, including 3D U-Net, RU-Net, DU-Net, and RDU-Net, before and after hepatectomy. Accurate liver volume measurement is critical in liver transplantation to ensure adequate functional recovery and minimize postoperative complications.

Non-invasive stroke diagnosis using speech data from dysarthria patients.

Acute Ischemic Stroke (AIS) is a major cause of disability and can lead to death in severe cases. A common symptom of AIS, dysarthria, significantly impacts the quality of life of patients. In this study, we developed a deep learning model using dysarthria data for cost-effective and non-invasive brain stroke diagnosis.

Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.

In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group 2: 434 teeth possible of immediate implant placement after extraction) for extraction were evaluated for the training and testing of a deep learning model. DenseNet121, ResNet18, ResNet101, ResNeXt101, InceptionNetV3, and InceptionResNetV2 were used.

Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.

Background: We aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.

Methods: Panoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0.

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.