AIMC Topic: Prospective Studies

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Interpretable machine learning model based on multimodal ultrasound for bedside diagnosis of acute exacerbations in COPD.

Respiratory research
BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with accelerated lung function decline and increased mortality. However, early and accurate diagnosis remains clinically challenging due to nonspecific s...

Clinical Efficacy of Real-Time Artificial Intelligence-Assisted Colonoscopy in Colorectal Polyp Detection: A Prospective Multicenter Randomized Controlled Trial.

Gut and liver
BACKGROUND/AIMS: Early detection and removal of colon polyps are critical for preventing colorectal cancer. Computer-aided detection (CADe) systems have been introduced to increase the polyp detection rate (PDR) during colonoscopy, potentially enhanc...

A noninvasive machine learning model using a complete blood count for screening of primary vitreoretinal lymphoma.

Nature communications
Primary vitreoretinal lymphoma (PVRL) is a rare and aggressive intraocular malignancy that is frequently misdiagnosed because of its nonspecific early manifestations and the lack of effective screening tools. We conduct a multicentre case-control stu...

Qualitative and quantitative assessment of accelerated liver diffusion-weighted imaging using deep-learning reconstruction in oncologic patients.

BMC medical imaging
BACKGROUND: Deep-learning (DL) reconstructions could improve image quality and reduce acquisition time in diffusion-weighted imaging (DWI). This study assessed, qualitatively and quantitatively, DL-DWI in liver metastasis of colorectal cancer patient...

Enhancing AI-based diabetic retinopathy diagnosis through universal cross-camera image adaptation.

BMJ open ophthalmology
OBJECTIVE: To evaluate the effectiveness of a deep learning-based style adaptation strategy in improving the diagnostic accuracy and cross-camera generalisability of artificial intelligence (AI) for detecting diabetic retinopathy (DR).

Artificial intelligence in anesthesia: comparison of the utility of ChatGPT v/s google gemini large language models in pre-anesthetic education: content, readability and sentiment analysis.

BMC anesthesiology
BACKGROUND: Large Language Models (LLMs) such as ChatGPT and Google Gemini are increasingly explored for their potential in patient education, particularly in the perioperative setting. As text-based tools trained on extensive datasets, they can gene...

Ability of the hypotension prediction index to predict hypotension in patients with septic shock in the intensive care unit.

Scientific reports
The hypotension prediction index (HPI) is a machine learning-based model for predicting hypotension. It provides good performance for predicting intraoperative hypotension but has rarely been studied in critically ill patients admitted to the intensi...

Machine learning-enhanced prediction of fetal growth restriction using fetal cardiac remodeling parameters.

BMC medicine
BACKGROUND: Fetal growth restriction (FGR) contributes to over 30% of late-pregnancy stillbirth, yet its diagnosis is challenging because current methods rely on indirect surrogate markers (estimated fetal weight and umbilical artery) that often fail...

Application of Narrative and AI-Assisted Follow-Up After Voluntary Medical Male Circumcision: Multicenter, Double-Blind, Prospective, Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: Postoperative anxiety following voluntary medical male circumcision (VMMC) poses a significant health challenge, with limited telemedicine access and inadequate communication compromising recovery and adherence. Narrative-based interventi...

Performance of the pediatric index of mortality (PIM-3) in a Moroccan PICU: challenges in resource-limited settings.

European journal of pediatrics
UNLABELLED: Prognostic scores such as the Pediatric Index of Mortality (PIM-3) are widely used to estimate mortality risk in PICUs, yet their performance in low- and middle-income countries (LMICs) remains uncertain. We aimed to evaluate the predicti...