Artificial Intelligence-based Liver Volume Measurement Using Preoperative and Postoperative CT Images.

Introduction: Accurate liver volumetry is crucial for hepatectomy. In this study, we developed and validated a deep learning system for automated liver volumetry in patients undergoing hepatectomy, both preoperatively and at 7 days and 3 months postoperatively.

Methods: A 3D U-Net model was trained on CT images from three time points using a five-fold cross-validation approach.

Comparison of segmentation performance of cnns, vision transformers, and hybrid networks for paranasal sinuses with sinusitis on CT images.

Accurate segmentation of the paranasal sinuses, including the frontal sinus (FS), ethmoid sinus (ES), sphenoid sinus (SS), and maxillary sinus (MS), plays an important role in supporting image-guided surgery (IGS) for sinusitis, facilitating safer intraoperative navigation by identifying anatomical variations and delineating surgical landmarks on CT imaging. To the best of our knowledge, no comparative studies of convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid networks for segmenting each paranasal sinus in patients with sinusitis have been conducted. Therefore, the objective of this study was to compare the segmentation performance of CNNs, ViTs, and hybrid networks for individual paranasal sinuses with varying degrees of anatomical complexity and morphological and textural variations caused by sinusitis on CT images.

Comparison of performance of cervical cancer grading based on acetowhite areas.

Cervical cancer ranks fourth globally in terms of both incidence and mortality among women, making timely diagnosis essential for effective treatment. Although the acetowhite regions and their margins are important for cervical cancer staging, their potential for automated cancer grading remains underexplored. This study aimed to enhance diagnostic accuracy and grading precision by effectively analyzing the acetowhite region and its surroundings.

CNN-Based Automatic Tablet Classification Using a Vibration-Controlled Bowl Feeder with Spiral Torque Optimization.

This paper proposes a drug classification system using convolutional neural network (CNN) training and rotational pill dropping technology. Images of 40 pills for each of 102 types (total 4080 images) were captured, achieving a CNN classification accuracy of 88.8%.

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.

Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images is important for stroke diagnosis, but most studies have focused on lesion segmentation using DWI. In this study, we compared the performance of lesion segmentation using DWI and ADC images.

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.

Performance Comparison of Machine Learning Using Radiomic Features and CNN-Based Deep Learning in Benign and Malignant Classification of Vertebral Compression Fractures Using CT Scans.

Distinguishing benign from malignant vertebral compression fractures is critical for clinical management but remains challenging on contrast-enhanced abdominal CT, which lacks the soft tissue contrast of MRI. This study evaluates and compares radiomic feature-based machine learning and convolutional neural network-based deep learning models for classifying VCFs using abdominal CT. A retrospective cohort of 447 vertebral compression fractures (196 benign, 251 malignant) from 286 patients was analyzed.

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.

Artificial Intelligence Model Assists Knee Osteoarthritis Diagnosis via Determination of K-L Grade.

Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. This study aims to automate the assessment of KOA severity by training deep learning models using the Kellgren-Lawrence grading system (class 0~4). A total of 15,000 images were used, with 3000 images collected for each grade.

Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery.

This study aims to predict the optimal imaging parameters using a deep learning algorithm in 3D heads-up vitreoretinal surgery and assess its effectiveness on improving the vitreoretinal surface visibility during surgery. To develop the deep learning algorithm, we utilized 212 manually-optimized still images extracted from epiretinal membrane (ERM) surgical videos. These images were applied to a two-stage Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) architecture.

Artificial intelligence for severity triage based on conversations in an emergency department in Korea.

In the fast-paced emergency departments, where crises unfold unpredictably, the systematic prioritization of critical patients based on a severity classification is vital for swift and effective treatment. This study aimed to enhance the quality of emergency services by automatically categorizing the severity levels of incoming patients using AI-powered natural language processing (NLP) algorithms to analyze conversations between medical staff and patients. The dataset comprised 1,028 transcripts of bedside conversations within emergency rooms.

Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to achieve better predictive performance than a single learning algorithm alone. Machine learning techniques, including EL, have been applied in the field of medicine to assist in the clinical interpretation of specific diseases. Although neurodegenerative diseases, especially Alzheimer's disease (AD), are of interest to clinicians and researchers due to their rapid increase in clinical cases, the application of EL in AD diagnosis has been relatively less attempted.

Deep-learning-based detection of large vessel occlusion: A comparison of CT and diffusion-weighted imaging.

Background: Rapid and accurate identification of large vessel occlusion (LVO) is crucial for determining eligibility for endovascular treatment. We aimed to validate whether computed tomography combined with clinical information (CT&CI) or diffusion-weighted imaging (DWI) offers better predictive accuracy for anterior circulation LVO.

Methods: Computed tomography combined with clinical information and DWI data from patients diagnosed with acute ischemic stroke were collected.

Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.

Osteoarthritis (OA) is the most common joint disease, affecting over 300 million people worldwide. Subchondral sclerosis is a key indicator of OA. Currently, the diagnosis of subchondral sclerosis is primarily based on radiographic images; however, reliability issues exist owing to subjective evaluations and inter-observer variability.

Automated Neonatal Hip Ultrasound System for Diagnosing Developmental Dysplasia of Hips Using Assistive AI.

This study aims to develop and evaluate an artificial intelligence (AI)-based diagnostic system for the diagnosis of developmental dysplasia of the hip (DDH) in infant hip ultrasonography. The Graf algorithm was employed to develop an automated model for diagnosing DDH, resulting in a DDH-assisted AI model with an average Graf angle error rate of 0.21 compared to expert diagnostics.

Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations.

Objective: Exacerbation of chronic respiratory diseases leads to poor prognosis and a significant socioeconomic burden. To address this issue, an artificial intelligence model must assess patient prognosis early and classify patients into high- and low-risk groups. This study aimed to develop a model to predict in-hospital mortality in patients with chronic respiratory disease using demographic, clinical, and environmental factors, specifically air pollution exposure levels.

Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness.

Introduction: Identifying factors that increase the risk of hospital readmission will help determine high-risk patients and decrease the socioeconomic burden. Pneumonia is associated with high readmission rates. Although residential greenness has been reported to have beneficial health effects, no studies have investigated its importance in predicting readmission in patients with pneumonia.

Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.

Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage and has a poor prognosis. This study aimed to develop an algorithm to detect tumors in esophageal endoscopy images using innovative artificial intelligence (AI) techniques for early diagnosis and detection of esophageal cancer. We used white light and narrowband imaging data collected from Gachon University Gil Hospital, and applied YOLOv5 and RetinaNet detection models to detect lesions.

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.

An Analysis of the Efficacy of Deep Learning-Based Pectoralis Muscle Segmentation in Chest CT for Sarcopenia Diagnosis.

Sarcopenia is the loss of skeletal muscle function and mass and is a poor prognostic factor. This condition is typically diagnosed by measuring skeletal muscle mass at the L3 level. Chest computed tomography (CT) scans do not include the L3 level.

Automated Imaging of Cataract Surgery Using Artificial Intelligence.

This study proposes a state-of-the-art technology to estimate a set of parameters to automatically display an optimized image on a screen during cataract surgery. We constructed an architecture comprising two stages to estimate the parameters for realizing the optimized image. The Pix2Pix approach was first introduced to generate fake images that mimic the optimal image.

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.

Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection.

Employing two standard mammography views is crucial for radiologists, providing comprehensive insights for reliable clinical evaluations. This study introduces paired mammogram view based-network(PMVnet), a novel algorithm designed to enhance breast lesion detection by integrating relational information from paired whole mammograms, addressing the limitations of current methods. Utilizing 1,636 private mammograms, PMVnet combines cosine similarity and the squeeze-and-excitation method within a U-shaped architecture to leverage correlated information.