AIMC Topic: Prospective Studies

Clear Filters Showing 261 to 270 of 2510 articles

Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

PLoS medicine
BACKGROUND: Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML]...

Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency.

JAMA network open
IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.

Feasibility of remote robot empowered teleultrasound scanning for radioactive patients.

Scientific reports
To investigate the feasibility of robot-assisted teleultrasound diagnosis for radioactive patients compared with conventional ultrasound diagnosis. In this prospective study (ChineseClinicalTrials.gov identifier, ChiCTR2200057253), 32 radioactive pat...

Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.

European radiology
OBJECTIVES: To exploit the capability of super-resolution deep learning reconstruction (SR-DLR) to save radiation exposure from coronary CT angiography (CCTA) and assess its impact on image quality, coronary plaque quantification and characterization...

Development and validation of an interpretable machine learning model to predict major adverse cardiovascular events after noncardiac surgery in geriatric patients: a prospective study.

International journal of surgery (London, England)
BACKGROUND: Major adverse cardiovascular events (MACEs) within 30 days following noncardiac surgery are prognostically relevant. Accurate prediction of risk and modifiable risk factors for postoperative MACEs is critical for surgical planning and pat...

External validation of 12 existing survival prediction models for patients with spinal metastases.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases may inform patients and clinicians in shared decision-making.

Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
OBJECTIVE: Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of imp...

Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.

BMC medical informatics and decision making
BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively ...

Machine learning to detect recent recreational drug use in intensive cardiac care units.

Archives of cardiovascular diseases
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.

BMC pregnancy and childbirth
BACKGROUND: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a l...