Surveillance

Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.

: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcom...

An Interventional Radiologist's Primer of Critical Appraisal of Artificial Intelligence Research.

Recent advances in artificial intelligence (AI) are expected to cause a significant paradigm shift in all digital data-driven aspects of information gain, processing, and decision making in both clinical healthcare and medical research. The field of interventional radiology (IR) will be enmeshed in this innovation, yet the collective IR expertise in the field of AI remains rudimentary because of l...

Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.

OBJECTIVE: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for s...

Effectiveness of an artificial intelligence-based training and monitoring system in prevention of nosocomial infections: A pilot study of hospital-based data.

This work describes a novel artificial intelligence-based training and monitoring system (AITMS) that was used to control and prevent nosocomial infec...

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation.

BACKGROUND: The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand a...

Artificial intelligence for dementia research methods optimization.

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological ch...

Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on -mediated lipid metabolism.

Deep learning is evolving in nutritional epidemiology to address challenges including precise nutrition and data-driven disease modeling. Fermented da...

A longitudinal observational study on the epidemiology of painful procedures and sucrose administration in hospitalized preterm neonates.

Although sucrose is widely administered to hospitalized infants for single painful procedures, total sucrose volume during the entire neonatal intensi...

Investigating the impact of structured reporting on the linguistic standardization of radiology reports through natural language processing over a 10-year period.

OBJECTIVES: To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and disti...

Imagining machine vision: Four visual registers from the Chinese AI industry.

Machine vision is one of the main applications of artificial intelligence. In China, the machine vision industry makes up more than a third of the nat...

Bias in artificial intelligence in vascular surgery.

Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning...

Fast dose calculation in x-ray guided interventions by using deep learning.

Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring syste...

An innovative ensemble model based on deep learning for predicting COVID-19 infection.

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare...

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach.

BACKGROUND: Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of informati...

Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer.

PURPOSE: Gastric cancer (GC) ranks as the 7th most common cancer worldwide and a leading cause of cancer mortality. In Iran, stomach malignancies are ...

Identifying inpatient mortality in MarketScan claims data using machine learning.

PURPOSE: Inpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hos...

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1).

In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim...

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies....

An FDA Guide on Indications for Use and Device Reporting of Artificial Intelligence-Enabled Devices: Significance for Pediatric Use.

Radiology has been a pioneer in adopting artificial intelligence (AI)-enabled devices into the clinic. However, initial clinical experience has identi...

Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction.

This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic...