Automatic image classification for the urinoculture screening

Computers in Biology and Medicine
Paolo AndreiniAlessandro Mecocci

Abstract

Urinary tract infections (UTIs) are considered to be the most common bacterial infection and, actually, it is estimated that about 150 million UTIs occur world wide yearly, giving rise to roughly $6 billion in healthcare expenditures and resulting in 100,000 hospitalizations. Nevertheless, it is difficult to carefully assess the incidence of UTIs, since an accurate diagnosis depends both on the presence of symptoms and on a positive urinoculture, whereas in most outpatient settings this diagnosis is made without an ad hoc analysis protocol. On the other hand, in the traditional urinoculture test, a sample of midstream urine is put onto a Petri dish, where a growth medium favors the proliferation of germ colonies. Then, the infection severity is evaluated by a visual inspection of a human expert, an error prone and lengthy process. In this paper, we propose a fully automated system for the urinoculture screening that can provide quick and easily traceable results for UTIs. Based on advanced image processing and machine learning tools, the infection type recognition, together with the estimation of the bacterial load, can be automatically carried out, yielding accurate diagnoses. The proposed AID (Automatic Infection Detector) sy...Continue Reading

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Citations

Feb 28, 2020·Clinical Microbiology Reviews·Olivier VandenbergAlex van Belkum
Jan 1, 2020·Computer Methods and Programs in Biomedicine·Paolo AndreiniFranco Scarselli
Apr 29, 2021·International Journal of General Medicine·Sheng YuanRuhai Bai

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