Institute for Biomedical Diagnostics and Research NALAZ

Artificial Intelligence (AI) has been drawing attention in the field of medical devices. However, due to system complexity, the variability of their architecture, as well as ethical and regulatory concerns there is an ongoing need to analyze its application and performance.This study presents a narrative commentary on the applications of artificial neural networks (ANN) and machine learning (ML) algorithms in medical devices, past, current and future perspectives of application. One research focus of this study was on identifying problems and issues related to the implementation of AI in medical devices. The commentary is based on scientific articles published in PubMed, Scopus ad ScienceDirect databases, official publications of international organizations: European Comission (EC), Food and Drug Administration (FDA), and World Health Organisation (WHO) published in 2009 - 2020 period. AI is revolutionizing healthcare, from medical applications to clinical engineering. However, before grasp-ing the full potential ethical, legal and social concerns need to be resolved and its application needs to be harmonized and regulated regarding equitable access, privacy, appropriate uses and users, liability and bias and inclusiveness.

Urinary Tract Infections (UTIs) are the most common type of bacterial infection and the physical, chemical and microscopic analysis of urine, followed by culture is a commonly conducted procedure in microbiological laboratories. Even though physical, chemical and microscopic analysis are more time efficient, culture is still considered the gold standard in diagnosing UTI, although its results and the results of antibiogram are obtained within a period of days. The aim of this research is to create a fuzzy model to predict the risk of urinary tract infections using microscopic and chemical urine analysis. A dataset containing 595 samples was used to create a fuzzy model using a Mamdani-type inference system with 5 inputs (nitrite, leukocyte esterase, bacteria, white blood cells and red blood cells) and 1 output (prediction of infection). The accuracy of the model achieved was 86%, after the contaminated samples were excluded. The recall value for predicting the absence of UTI samples was the highest, 89%, while the recall value for predicting the presence of UTI was 71%. The lowest recall achieved was for samples that could present both, the absence and the presence of UTI, with a value of 29.6%. The model was successful and revealed adequate accuracy, however future experiments should use more parameters.

Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Human papillomavirus (HPV) is one of the most common sexually transmitted disease (STD) worldwide. HPV is a small, double-stranded DNA virus that infects mucosal and coetaneous epithelia trough tiny cuts and abrasion, exposed by the cells of the basal layer. If diagnosed on time HPV can be successfully treated, however, in some cases it can lead to the development of tumor. Most of cervical tumors contain HPV DNA, and majority of them contain high-risk types HPV16 and HPV18. Different risk factors are associated with HPV infection, including behavioral and biological predispositions. Aim of this study is to genotype potentially infected patients on high-risk types HPV DNA and to correlate the results with patient’s different biological and lifestyle factors. For this purpose 20 gynecological smear samples were collected from women, previously subjected to the survey. Methodology included DNA extraction and real-time polymerase chain reaction (RT-PCR). Results showed that out of 20 patients five were positive for high risk HPV. Four of five positive patients were positive on HPV16 type of which one had HPV16 together with others high risk types. One of five positive patients was positive on HPV18 type and other high risk types not identified. Final outcome indicates the correlation of potentially endangered patients with specific sexual behavior and lifestyles, and furthermore represent the general consensus and awareness level this disease has on the public