Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application

Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window

Diagnostic approach to Helicobacter pylori-related gastric oncogenesis.

Helicobacter pylori (H. pylori) is a causative agent of peptic ulcer disease and plays an important role in the development of various other upper and lower gastrointestinal tract and systemic diseases; in addition to carcinogenesis and the development of mucosa-associated lymphoid tissue lymphoma, extragastric manifestations of H. pylori are increasingly being unraveled. Therefore, prompt and accurate diagnosis is essential. Within this narrative review we present an overview of the current trend in the diagnosis of H. pylori infection and its potential oncogenic sequelae, including gastric mucosa atrophy, intestinal metaplasia, dysplasia and gastric cancer. Signs of H. pylori-related gastric cancer risk can be assessed by endoscopy using the Kyoto classification score. New technology, such as optical or digital chromoendoscopy, improves diagnostic accuracy and provides information regarding H. pylori-related gastric preneoplastic and malignant lesions. In addition, a rapid urease test or histological examination should be performed, as these offer a high diagnostic sensitivity; both are also useful for the diagnosis of sequelae including gastric and colon neoplasms. Culture is necessary for resistance testing and detecting H. pylori-related gastric dysbiosis involved in gastric oncogenesis. Likewise, molecular methods can be utilized for resistance testing and detecting H. pylori-related gastric cancer development and progression. Noninvasive tests, such as the urea breath and stool antigen tests, can also be implemented; these are also suitable for monitoring eradication success and possibly for detecting H. pylori-related gastric malignancy. Serological tests may help to exclude infection in specific populations and detect gastric and colon cancers. Finally, there are emerging potential diagnostic biomarkers for H. pylori-related gastric cancer

Urinary Tract Infection Analysis using Machine Learning based Classification and ANN- A Study of Prediction

Urinary tract infection is the most frequently diagnosed infection among humans. A urinary tract infection (UTI) affects the areas of urinary system which includes the ureters, bladder, kidneys and urethra. The primary infected area of urinary system involves the lower tract i.e. bladder and urethra. The infection in bladder is painful as well as uncomfortable but if it spreads to kidneys, it can have severe consequences. Women are more susceptible to urinary infection in comparison to men due to their physiology. This paper aims to study and assess the impact and causes of urinary tract infection in human beings and evaluate the machine learning approach for urinary disease forecasting. The paper also proposed machine learning based methodology for the prediction of the urinary infection and estimating the outcomes of the designed procedures over real-time data and validating the same. The paper focuses to get high prediction accuracy of UTI using confusion matrix by Machine Based Classification and ANN technique. Some specific parameters have been selected with the help of Analysis of variance technique. The naive bayes classifier, J48 decision tree algorithm, and Artificial neural network have been used for the prediction of presence of urinary infection. The accuracy achieved by the proposed model is 95.5% approximately

On the application of optical forward-scattering to bacterial identification in an automated clinical analysis perspective

The Optical Forward Scattering (OFS) technique can be used to identify pathogens by direct observation of bacteria colonies growing on a culture plate. The identification is based on the acquisition of scattering images from isolated colonies and their subsequent comparison with reference images acquired from known bacteria. The technique has been mainly studied for the identification of pathogens in the food-safety field. This paper focuses on the possibility of extending the applicability of the technique to the field of clinical laboratory automation. This scenario requires that the paradigm of image acquisition at fixed colony-dimension, well established in the food-safety applications, should be substituted by an acquisition at fixed incubation time. As a consequence, the scatterometer must be adjustable in real-time for adapting to the actual features of the bacterial colony. The paper describes an OFS system prototype qualified by the possibility to tune both the laser beam diameter and the acquisition camera field of view. Preliminary experiments on bacteria cultures from pathogens causing infections of the urinary tract show that the proposed approach is promising for the development of an automated bacteria identification station. The new OFS approach also involves an alternative method for building a reference image database for subsequent image analysis

The Axis “Human Papillomavirus - Anal Squamous Cell Carcinoma”: A Review

Background: Anal Squamous Cell Carcinoma (ASCC) is an infrequent neoplasia that represents 2% of the digestive tumors and it has a growing incidence. Objective: This investigation (i) studies the pathogenesis of an increasingly prevalent disease, (ii) its treatment and prognosis along with (iii) a bibliographical review of the main characteristics of the Human Papillomavirus (HPV) as well as its effects on humans. Methods: A literature review is performed, comprising articles up to 2019 and cross-research manuscripts with the initial research. Results: Several studies demonstrate the HPV role as a significant risk factor to the development of ASCC, as well as its higher incidence in HIV-positive individuals and in those who engage in receptive anal intercourse. Future trends in theragnostic using information technology are examined. Conclusions: ASCC is a neoplasm mostly associated with HPV. Many studies are needed to improve the treatment as well as in the evaluation of the tumor characteristics

The feasibility of using of electronic health records to inform clinical decision making for community-onset urinary tract infection in England

Urinary tract infections (UTIs) are a major source of morbidity, yet differentiating UTI from other conditions and choosing the right treatment remains challenging. Using case studies from English primary and secondary care, this thesis investigates the potential use of electronic health records (EHR) - i.e., data recorded as part of routine care - to aid the diagnosis and management of community-onset UTI. I start by introducing sources of uncertainty in diagnosing UTI (Chapter 1) and review how EHRs have previously been used to study UTIs (Chapter 2). In Chapter 3, I discuss EHR sources available to study UTIs in England. In Chapter 4, I explore how EHRs from primary care can be used to guide antibiotic prescribing for UTI, by evaluating harms of delaying treatment in key patient groups. In Chapters 5 and 6, I explore the use of EHR data as a diagnostic tool to guide antibiotic de-escalation in patients with suspected UTI in the emergency department (ED). Cases of community-onset UTI could be identified in both primary and secondary care data but case definitions relied heavily on coarse diagnostic codes. A lack of information on patients' acute health status, clinical observations (e.g., urine dipstick tests), and reasons for antibiotic prescribing resulted in heterogeneous study cohorts, which likely confounded estimated effects of antibiotic treatment in primary care. In secondary care, early prediction of bacteriuria to guide antibiotic prescribing decisions in the ED proved promising, but model performance varied greatly by patient mix and variable definitions. Better recording of clinical information and a combination of retrospective EHR analysis with prospective cohorts and qualitative approaches will be required to derive actionable insights on UTI. Results based solely on currently available EHR data need to be interpreted carefully