Thermal imaging and deep learning-based fit-checking for respiratory protection.

This study develops an artificial intelligence model to quickly and easily determine correct mask-wearing in real time using thermal videos that ascertained temperature changes caused by air trapped inside the mask. Five types of masks approved by the Korean Ministry of Food and Drug Safety were worn in four different ways across 50 participants, generating 5000 videos. The results showed that 3DCNN outperformed ConvLSTM in both binary and multi-classification for mask wearing methods, with the highest AUROC of 0.

Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient Samples from a Tertiary Health Promotion Center in Korea.

A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877.

Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes.

This study introduces a novel approach for generating machine-generated instruction datasets for fine-tuning medical-specialized language models using MIMIC-IV discharge records. The study created a large-scale text dataset comprising instructions, cropped discharge notes as inputs, and outputs in JSONL format. The dataset was generated through three main stages, generating instruction and output using seed tasks provided by medical experts, followed by invalid data filtering.

Well-Tempered Medical Prompt Engineering for Explainable Extubation.

This study investigated whether the large language model (LLM) utilizes sufficient domain knowledge to reason about critical medical events such as extubation. In detail, we tested whether the LLM accurately comprehends given tabular data and variable importance and whether it can be used in complement to existing ML models such as XGBoost.

Assessing the Proficiency of LLMs with Various Tasks and Evaluators.

Previous studies have been limited to giving one or two tasks to Large Language Models (LLMs) and involved a small number of evaluators within a single domain to evaluate the LLM's answer. We assessed the proficiency of four LLMs by applying eight tasks and evaluating 32 results with 17 evaluators from diverse domains, demonstrating the significance of various tasks and evaluators on LLMs.

Fill in the Blank, MIMIC the Procedure: Tracking Patient Record for Medical Data Reconstruction.

This study addresses the missing data problem in the large-scale medical dataset MIMIC-IV, especially in situations where intubation-extubation events are paired. We employed a strategy involving patient scenario works that checked the temporal order and logical links of intubation/extubation data, and seven reconstruction rules for handling missing values. Through this, we reduced the overall loss rate from 36.

Comparison of AI with and without hand-crafted features to classify Alzheimer's disease in different languages.

Background: Detecting and analyzing Alzheimer's disease (AD) in its early stages is a crucial and significant challenge. Speech data from AD patients can aid in diagnosing AD since the speech features have common patterns independent of race and spoken language. However, previous models for diagnosing AD from speech data have often focused on the characteristics of a single language, with no guarantee of scalability to other languages.

Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images.

This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac).

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm.

Backgruound: Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea.

Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer.

Background: Improvement in survival in patients with advanced cancer is accompanied by an increased probability of bone metastasis and related pathologic fractures (especially in the proximal femur). The few systems proposed and used to diagnose impending fractures owing to metastasis and to ultimately prevent future fractures have practical limitations; thus, novel screening tools are essential. A CT scan of the abdomen and pelvis is a standard modality for staging and follow-up in patients with cancer, and radiologic assessments of the proximal femur are possible with CT-based digitally reconstructed radiographs.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness.

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included.

Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia.

Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients.

Estimation of low-density lipoprotein cholesterol levels using machine learning.

Background: Low-density lipoprotein-cholesterol (LDL-C) is used as a threshold and target for treating dyslipidemia. Although the Friedewald equation is widely used to estimate LDL-C, it has been known to be inaccurate in the case of high triglycerides (TG) or non-fasting states. We aimed to propose a novel method to estimate LDL-C using machine learning.

Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image.

Background: Early detection of asbestosis is important; hence, quick and accurate diagnostic tools are essential. This study aimed to develop an algorithm that combines lung segmentation and deep learning models that can be utilized as a clinical decision support system (CDSS) for diagnosing patients with asbestosis in segmented computed tomography (CT) images.

Methods: We accurately segmented the lungs in CT images of patients examined at Seoul St.

Preparation of Network-Structured Carbon Nanofiber Mats Based on PAN Blends Using Electrospinning and Hot-Pressing Methods for Supercapacitor Applications.

In this work, we prepared network-structured carbon nanofibers using polyacrylonitrile blends (PAN150 and PAN85) with different molecular weights (150,000 and 85,000 g mol) as precursors through electrospinning/hot-pressing methods and stabilization/carbonization processes. The obtained PAN150/PAN85 polymer nanofibers (PNFs; PNF-73, PNF-64 and PNF-55) with different weight ratios of 70/30, 60/40 and 50/50 () provided good mechanical and electrochemical properties due to the formation of physically bonded network structures between the blended PAN nanofibers during the hot-processing/stabilization processes. The resulting carbonized PNFs (cPNFs; cPNF-73, cPNF-64, and cPNF-55) were utilized as anode materials for supercapacitor applications.

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis.

Background: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined.

Objectives: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality.

Satisfaction and Usability of an Information and Communications Technology-Based System by Clinically Healthy Patients With COVID-19 and Medical Professionals: Cross-sectional Survey and Focus Group Interview Study.

Background: Digital health care is an important strategy in the war against COVID-19. South Korea introduced living and treatment support centers (LTSCs) to control regional outbreaks and care for patients with asymptomatic or mild COVID-19. Seoul National University Hospital (SNUH) introduced information and communications technology (ICT)-based solutions to manage clinically healthy patients with COVID-19.

Privacy-Oriented Technique for COVID-19 Contact Tracing (PROTECT) Using Homomorphic Encryption: Design and Development Study.

Background: Various techniques are used to support contact tracing, which has been shown to be highly effective against the COVID-19 pandemic. To apply the technology, either quarantine authorities should provide the location history of patients with COVID-19, or all users should provide their own location history. This inevitably exposes either the patient's location history or the personal location history of other users.

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning.

The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L(36), which means that 3 × 6 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR).

Information Technology-Based Management of Clinically Healthy COVID-19 Patients: Lessons From a Living and Treatment Support Center Operated by Seoul National University Hospital.

Background: South Korea took preemptive action against coronavirus disease (COVID-19) by implementing extensive testing, thorough epidemiological investigation, strict social distancing, and rapid treatment of patients according to disease severity. The Korean government entrusted large-scale hospitals with the operation of living and treatment support centers (LTSCs) for the management for clinically healthy COVID-19 patients.

Objective: The aim of this paper is to introduce our experience implementing information and communications technology (ICT)-based remote patient management systems at a COVID-19 LTSC.