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Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study.

BMC gastroenterology
BACKGROUND: The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H....

Sex estimation using skull silhouette images from postmortem computed tomography by deep learning.

Scientific reports
Prompt personal identification is required during disasters that can result in many casualties. To rapidly estimate sex based on skull structure, this study applied deep learning using two-dimensional silhouette images, obtained from head postmortem ...

Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections.

Scientific reports
The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning t...

Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.

Scientific reports
Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechanics, and it provides a lot of information like the risk level caused by any vascular occlusion. Since WSS cannot be measured directly and other availab...

Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.

Scientific reports
The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when the symptoms may be extremely subtle. This study aimed to assess the accuracy of a convolutional neur...

Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings.

Scientific reports
This study evaluates the diagnostic efficacy of automated machine learning (AutoGluon) with automated feature engineering and selection (autofeat), focusing on clinical manifestations, and a model integrating both clinical manifestations and CT findi...

Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.

Scientific reports
Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the elderly, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its m...

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.

Journal of medical Internet research
BACKGROUND: Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines proposed by the American College of Surgeons have proven to be relatively insensitive when identifying se...

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach.

JMIR cardio
BACKGROUND: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are ...

Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs.

PLoS neglected tropical diseases
BACKGROUND: Conventional microscopy of Kato-Katz (KK1.0) thick smears, the primary method for diagnosing soil-transmitted helminth (STH) infections, has limited sensitivity and is error-prone. Artificial intelligence-based digital pathology (AI-DP) m...