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 Direct Anterior Approach and Posterolateral Approach in Total Hip Arthroplasty in Elderly People Aged 75 Years or Older.

Purpose: The study investigated the benefits of the direct anterior approach (DAA) compared to the posterolateral approach (PLA) in patients over 75 years of age.

Materials And Methods: This study included 144 patients who underwent total hip arthroplasty (THA) from December 2012 to November 2021. Group A had 93 patients with a mean age of 80.

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.

ATG7 in innate immune cells is required for host defense against nontuberculous mycobacterial pulmonary infections.

Infections caused by nontuberculous mycobacteria, such as Mycobacterium avium and Mycobacteroides abscessus, are becoming increasingly prevalent, and rising antibiotic resistance poses a significant clinical challenge. However, the mechanisms by which the host defense system controls these infections remain poorly understood. Here we show that the autophagy-related protein ATG7 in innate immune cells plays an essential role in controlling nontuberculous mycobacterial infection and protecting lung tissue from pathological inflammation.

Clinical and histopathological characteristics of melanomas in Asians under age 40.

Previous studies suggested racial difference between young melanomas of Caucasians and non-Caucasians. This study aimed to elucidate characteristics of melanomas in young Asians. We analyzed clinical and histologic characteristics of patients under age 40 diagnosed with cutaneous melanomas including in situs.

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.

Observations of CO Activation on the PdIn(111) Alloy Surface at Ambient Pressures.

The CO activation process has been investigated on the palladium-indium (PdIn) (111) alloy surface using ambient pressure scanning tunneling microscopy (AP-STM) and synchrotron-based X-ray photoelectron spectroscopy (AP-XPS). Pd and In atoms diffuse onto the topmost layer after annealing at 840 K, which adopts intermetallic PdIn alloy geometries in an ultrahigh vacuum. AP-STM reveals that interfacial Pd-InO nanostructures are created on the alloy surface by dissociative CO adsorption under CO(g) environments even at 300 K.

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.

Long-term use of dutasteride to treat androgenic alopecia in young men may lead to persistent abnormalities in semen parameters.

Objective: To evaluate the long-term effects of dutasteride on male fertility and determine the cutoff treatment duration that causes significant and persistent decreases in semen parameters.

Methods: This was a single-center, randomized, controlled study that evaluated 200 men (ages 28 to 39 years). Forty men were allocated to each study group, divided according to the duration of dutasteride treatment, as follows: <6 months (group 1), 6-12 months (group 2), 13-18 months (group 3), 19-24 months (group 4), and >24 months (group 5).

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.