AIMC Topic: Middle Aged

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The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

International heart journal
Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from E...

Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.

Technology in cancer research & treatment
To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the Cycle...

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data.

Technology in cancer research & treatment
Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invas...

Characterizing Opioid Overdoses Using Emergency Medical Services Data : A Case Definition Algorithm Enhanced by Machine Learning.

Public health reports (Washington, D.C. : 1974)
OBJECTIVES: Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (...

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.

Cancer control : journal of the Moffitt Cancer Center
INTRODUCTION: Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within le...

A UK-Wide Study Employing Natural Language Processing to Determine What Matters to People about Brain Health to Improve Drug Development: The Electronic Person-Specific Outcome Measure (ePSOM) Programme.

The journal of prevention of Alzheimer's disease
BACKGROUND: It is important to use outcome measures for novel interventions in Alzheimer's disease (AD) that capture the research participants' views of effectiveness. The electronic Person-Specific Outcome Measure (ePSOM) development programme is un...

Oncological and Functional Outcomes of Robot-Assisted Radical Prostatectomy in Kidney Transplant Recipients.

JSLS : Journal of the Society of Laparoendoscopic Surgeons
OBJECTIVE: Management of prostate cancer in kidney transplant recipients presents a unique surgical challenge due to the risk of direct or indirect injury to the transplanted kidney. Herein, we report the largest single center study of Robot-assisted...

Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.

Technology in cancer research & treatment
This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). A total of 28 patients with HL and 101 patients with ICCA were...

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test.

Journal of Alzheimer's disease : JAD
BACKGROUND: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to deve...