AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Diagnostic Tests, Routine

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UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

PloS one
BACKGROUND: Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomed...

Digital microbiology.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
BACKGROUND: Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient us...

Imaging of OA - From disease modification to clinical utility.

Best practice & research. Clinical rheumatology
Multiple disease-modifying osteoarthritis drug (DMOAD) trials were done in the last two decades, but no pharmacological agent has yet been approved by regulatory agencies as an effective therapy to date. Given the fact that we have seen the recent di...

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

Journal of medical Internet research
BACKGROUND: Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no establis...

Performance evaluation of machine learning-based infectious screening flags on the HORIBA Medical Yumizen H550 Haematology Analyzer for vivax malaria and dengue fever.

Malaria journal
BACKGROUND: Automated detection of malaria and dengue infection has been actively researched for more than two decades. Although many improvements have been achieved, these solutions remain too expensive for most laboratories and clinics in developin...

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

PLoS neglected tropical diseases
BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patien...

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

BMC medicine
BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colp...

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.

Parasites & vectors
BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST s...

Artificial intelligence in dermatopathology: Diagnosis, education, and research.

Journal of cutaneous pathology
Artificial intelligence (AI) utilizes computer algorithms to carry out tasks with human-like intelligence. Convolutional neural networks, a type of deep learning AI, can classify basal cell carcinoma, seborrheic keratosis, and conventional nevi, high...

Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis.

Surgical endoscopy
BACKGROUND: In the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic proce...