AIMC Topic: Diagnostic Tests, Routine

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Are malaria rapid diagnostic test results stable over time to support verification of surveillance data?

Malaria journal
BACKGROUND: Rapid diagnostic tests (RDTs) have improved malaria case management by enabling point-of-care confirmation of infection, particularly in low-resource settings. In addition to clinical use, RDT results recorded in health facility registers...

Rapid discrimination of and non-tuberculous mycobacteria disease via interpretive machine learning analysis of routine laboratory tests.

BMJ health & care informatics
OBJECTIVES: Rapid discrimination of infections caused by (MTB) and non-tuberculous mycobacteria (NTM) is crucial in clinical settings. Despite overlapping clinical and radiological features, the two require markedly different therapeutic approaches ...

Evaluating the performance of an artificial intelligence-based electronic reader for malaria rapid diagnostic tests across Benin, Côte d'Ivoire, Nigeria and Uganda.

Malaria journal
BACKGROUND: The introduction of malaria rapid diagnostic tests (RDTs) has expanded the parasitological confirmation of malaria at all levels of health systems in sub-Saharan Africa, improving case management and surveillance. However, concerns persis...

Machine-learning-based artificial intelligence tools for the diagnosis of tropical fevers: a systematic review and meta-analysis protocol of diagnostic test accuracy.

BMJ open
INTRODUCTION: Recent advancements in diagnosing tropical fevers increasingly use artificial intelligence (AI). These innovations focus on diagnosing single or multiple diseases, significantly reducing the global burden of tropical fevers. This protoc...

AI-enhanced rapid diagnostic testing platform for mass opisthorchiasis screening.

Scientific reports
Cholangiocarcinoma (CCA) is a prevalent malignancy in countries along Mekong basin, closely linked to chronic infections caused by Opisthorchis viverrini (OV). Early detection of OV-infected individuals holds significant promise for screening at-risk...

Validation of Vetscan Imagyst, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Parasites & vectors
BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intell...

Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice.

Laboratory investigation; a journal of technical methods and pathology
The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the pathology community. This review article aimed to outline the latest developments in digital pathology, the...

Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis.

Clinical oral investigations
OBJECTIVES: Temporomandibular disorders (TMDs) are the second most common musculoskeletal condition which are challenging tasks for most clinicians. Recent research used machine learning (ML) algorithms to diagnose TMDs intelligently. This study aime...

The Use of Uroflowmetry as a Diagnostic Test.

Current urology reports
PURPOSE OF REVIEW: Uroflowmetry is widely used for initial non-invasive evaluation of lower urinary tract disorders. Current clinical use is mostly restricted to a scrutiny of the maximum flow rate and uroflow pattern recorded by a conventional flowm...