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Prospective Studies

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Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.

BMC anesthesiology
BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient ...

Investigating the mechanisms of internet gaming disorder and developing intelligent monitoring models using artificial intelligence technologies: protocol of a prospective cohort.

BMC public health
BACKGROUND: Internet gaming disorder (IGD), recognized by the World Health Organization (WHO), significantly impacts adolescent mental and physical health. With a global prevalence of 3.05%, rates are higher in Asia, especially among adolescents and ...

[Development and validation of a tool for the systematic identification of social vulnerabilities in cancer patients: the DEFCO tool].

Bulletin du cancer
INTRODUCTION: Literature suggests that patients from deprived backgrounds are less likely to adhere to their treatments, continue to expose themselves to risk factors and, as a result, have poorer health outcomes. It is therefore crucial to identify ...

Enhancing trainee performance in obstetric ultrasound through an artificial intelligence system: randomized controlled trial.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Performing obstetric ultrasound scans is challenging for inexperienced operators; therefore, the prenatal screening artificial intelligence system (PSAIS) software was developed to provide real-time feedback and guidance for trainees durin...

Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation.

BMC oral health
OBJECTIVE: This clinical study aimed to evaluate the practical value of integrating an AI diagnostic model into clinical practice for caries detection using intraoral images.

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

Magnetic resonance in medicine
PURPOSE: To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.

Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting.

Ophthalmology. Glaucoma
PURPOSE: This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence ...

The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
INTRODUCTION: Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical ...

A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study.

BMC medicine
BACKGROUND: The pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment...