AIMC Topic: Diagnostic Tests, Routine

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Automated detection of Mycobacterium tuberculosis using transfer learning.

Journal of infection in developing countries
INTRODUCTION: Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges...

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations.

Analytica chimica acta
The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impract...

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.

Pain research & management
PURPOSE: The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandu...

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies.

Research synthesis methods
Updating systematic reviews is often a time-consuming process that involves a lot of human effort and is therefore not conducted as often as it should be. The aim of our research project was to explore the potential of machine learning methods to red...

Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives.

Analytical chemistry
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics...

Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data.

PloS one
Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application ...

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...

Leveraging digital media data for pharmacovigilance.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharmacovigilance is essential as drugs often have under-reported side effects. This study intends to use ...