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Emerging Technologies in the Treatment of Adult Spinal Deformity

Patel AV, White CA, Schwartz JT, Pitaro NL, Shah KC, Singh S, Arvind V, Kim JS, Cho SK

Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patientspecific rods are tools that are being used...
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Construction of a Standard Dataset for Liver Tumors for Testing the Performance and Safety of Artificial Intelligence-Based Clinical Decision Support Systems

Kim Ss, Lee DH, Lee MW, Kim SY, Shin J, Choi J, Choi BW

Purpose To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials...
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A Study on the Relationship Between Mental Health Variables and Physical Activity Variables in the Clinical Group of North Korean Defectors: A Pilot Study

Shim SS, Lee SH, Lee JB, Seo YE, Lee HJ

Objectives This study is designed to extract a representative variable that distinguishes psychiatric patients of North Korean Defectors and a control group by using machine learning based on measured mental...
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Machine Learning Techniques in Prostate Cancer Diagnosis According to Prostate-Specific Antigen Levels and Prostate Cancer Gene 3 Score

Passera R, De Luca S, Fiori C, Bollito E, Porpiglia F

Purpose: To explore the role of artificial intelligence and machine learning (ML) techniques in oncological urology. In recent years, our group investigated the prostate cancer gene 3 (PCA3) score, prostate-specific...
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An Implementation of Natural Language Processing and Text Mining in Stroke Research

Kim C

Natural language processing (NLP) is a computerized approach to analyzing text that explores how computers can be used to understand and manipulate natural language text or speech to do useful...
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Machine Learning Approach for Active Vaccine Safety Monitoring

Kim Y, Jang JH, Park N, Jeong NY, Lim E, Kim S, Choi NK, Yoon D

Background: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is...
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Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

Gómez KAH, Echeverry-Correa JD, Gutiérrez

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in...
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Machine Learning for Antibiotic Resistance Prediction: A Prototype Using Off-the-Shelf Techniques and Entry-Level Data to Guide Empiric Antimicrobial Therapy

Feretzakis G, Sakagianni A, Loupelis E, Kalles D, Skarmoutsou N, Martsoukou M, Christopoulos C, Lada M, Petropoulou S, Velentza A, Michelidou S, Chatzikyriakou R, Dimitrellos E

Objectives: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As...
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Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications

Tougui I, Jilbab A, Mhamdi JE

Objectives: With advances in data availability and computing capabilities, artificial intelligence and machine learning technologies have evolved rapidly in recent years. Researchers have taken advantage of these developments in healthcare...
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Evaluation and Management of Dysphagia Based on Digital Health Technologies

Lee WH

The need for non-contact practice during the COVID-19 pandemic has resulted in a rapidly growing interest in digital health technologies (DHTs). Until recently, swallowing evaluations and treatments have been performed...
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Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models

Heo JH, Kim T, Shin J, Suh GJ, Kim J, Jung YS, Park SM, Kim S, For SNU CARE investigators

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC...
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Automatic Classification of the Korean Triage Acuity Scale in Simulated Emergency Rooms Using Speech Recognition and Natural Language Processing: a Proof of Concept Study

Kim D, Oh J, Im H, Yoon M, Park J, Lee J

Background: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea. For rapid triage, we...
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Artificial Intelligence and Echocardiography

Yoon YE, Kim S, Chang HJ

Artificial intelligence (AI) is evolving in the field of diagnostic medical imaging, including echocardiography. Although the dynamic nature of echocardiography presents challenges beyond those of static images from X-ray, computed...
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Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TML, Hsieh B, Choi JW, Wang D, Vallières M, Wang R, Collins S, Feng X, Feldman M, Zhang PJ, Atalay M, Sebro R, Yang L, Fan Y, Liao Wh, Bai HX

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials...
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LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer

Kang J, Choi YJ, Kim Ik, Lee HS, Kim H, Baik SH, Kim NK, Lee KY

Purpose The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning–based approach...
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Automated Prediction of Ischemic Brain Tissue Fate from Multiphase Computed Tomographic Angiography in Patients with Acute Ischemic Stroke Using Machine Learning

Qiu W, Kuang H, Ospel JM, Hill MD, Demchuk AM, Goyal M, Menon BK

Background and Purpose Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML)...
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The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study

Akazawa M, Hashimoto K, Noda K, Yoshida K

Objective Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic...
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Social Network Analysis of an Online Smoking Cessation Community to Identify Users’ Smoking Status

Shah AM, Yan X, Qayyum A

Objectives: Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in...
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Radiomics of Non-Contrast-Enhanced T1 Mapping: Diagnostic and Predictive Performance for Myocardial Injury in Acute ST-Segment-Elevation Myocardial Infarction

Ma Q, Ma Y, Yu T, Sun Z, Hou Y

Objective: To evaluate the feasibility of texture analysis on non-contrast-enhanced T1 maps of cardiac magnetic resonance (CMR) imaging for the diagnosis of myocardial injury in acute myocardial infarction (MI). Materials and...
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Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

Qiu K, Lee J, Kim H, Yoon S, Kang K

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for...
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