AIMC Topic: Proof of Concept Study

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Proof-of-concept evaluation at Cox's Bazar of the Safe Water Optimization Tool: water quality modelling for safe water supply in humanitarian emergencies.

BMJ global health
INTRODUCTION: Waterborne diseases are leading concerns in emergencies. Humanitarian guidelines stipulate universal water chlorination targets, but these fail to reliably protect water as postdistribution chlorine decay can leave water vulnerable to p...

Liver MRI proton density fat fraction inference from contrast enhanced CT images using deep learning: A proof-of-concept study.

PloS one
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases make...

Leveraging deep learning for the detection of socially desirable tendencies in personnel selection: A proof-of-concept.

PloS one
We propose a deep learning-based method for detecting Socially Desirable Responding (SDR)-the tendency for individuals to distort questionnaire responses to present themselves in a favorable light. Our objective is to showcase that such novel methods...

Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study.

PloS one
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated....

Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study.

mSystems
Colorectal cancer (CRC) is the third most common cancer, and it can be prevented by performing early screening. As a hallmark of cancer, the human microbiome plays important roles in the occurrence and development of CRC. Recently, the blood microbio...

Transformer-based deep learning enables improved B-cell epitope prediction in parasitic pathogens: A proof-of-concept study on Fasciola hepatica.

PLoS neglected tropical diseases
BACKGROUND: The identification of B-cell epitopes (BCEs) is fundamental to advancing epitope-based vaccine design, therapeutic antibody development, and diagnostics, such as in neglected tropical diseases caused by parasitic pathogens. However, the s...

Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study.

Journal of medical Internet research
BACKGROUND: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in health care include data augmentation by variational au...

Assessing Patient-Reported Satisfaction With Care and Documentation Time in Primary Care Through AI-Driven Automatic Clinical Note Generation: Protocol for a Proof-of-Concept Study.

JMIR research protocols
BACKGROUND: Relisten is an artificial intelligence (AI)-based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between health care professionals and patients. This tool extracts relevant infor...

Machine Vision Augmentation to Detect Detrusor Overactivity in Overactive Bladder: A Frontier of Artificial Intelligence Application in Functional Urology-Proof of Concept Clinical Study.

Neurourology and urodynamics
INTRODUCTION: Overactive bladder (OAB) is a common urological condition with increasing prevalence, especially in an aging population. Diagnosing and treating OAB can be challenging. While urodynamic study (UDS) is useful to confirm involuntary detru...

Machine Learning Models predicting Decompensation in Cirrhosis.

Journal of gastrointestinal and liver diseases : JGLD
BACKGROUND AND AIMS: Decompensation of cirrhosis significantly decreases survival, thus, prevention of complications is paramount. We used machine learning techniques to identify parameters predicting decompensation.