OBJECTIVEThis study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.STUDY DESIGNA systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.RESULTSThe vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.CONCLUSIONSCytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.
Objective. Nowadays numerous ancillary techniques detecting HPV DNA and mRNA compete with cytology; however no perfect test exists; in this study we evaluated classification and regression trees (CARTs) for the production of triage rules and estimate the risk for cervical intraepithelial neoplasia (CIN) in cases with ASCUS+ in cytology. Study Design. We used 1625 cases. In contrast to other approaches we used missing data to increase the data volume, obtain more accurate results, and simulate real conditions in the everyday practice of gynecologic clinics and laboratories. The proposed CART was based on the cytological result, HPV DNA typing, HPV mRNA detection based on NASBA and flow cytometry, p16 immunocytochemical expression, and finally age and parous status. Results. Algorithms useful for the triage of women were produced; gynecologists could apply these in conjunction with available examination results and conclude to an estimation of the risk for a woman to harbor CIN expressed as a probability. Conclusions. The most important test was the cytological examination; however the CART handled cases with inadequate cytological outcome and increased the diagnostic accuracy by exploiting the results of ancillary techniques even if there were inadequate missing data. The CART performance was better than any other single test involved in this study.
During the last decade, there is an increasing need for quality improvement of medical laboratories via the use of quality related standards. Recently regulatory bodies suggest and sometimes enforce the application of ISO 15189, which is designed especially for medical laboratories. Despite the standard does oblige the application of Laboratory Information Systems (LISs), it is evident that without a LIS it is difficult for laboratories to operate efficiently. Modern cytopathology laboratories form complex systems composed of a multidisciplinary human team coupled with medical modalities and capabilities. Hopefully, such laboratories have well standardized and defined workflow. The adoption of the standard, creates numerous management requirements, introduces new functions and associated overhead. In this paper, we present design and implementation issues of an enhanced LIS to support ISO 15189 in a cytopathology laboratory. The LIS designed around ISO 15189 management requirements can improve, enhance and facilitate the standard application and adoption.
This article describes how the use of artificial intelligence applications as a consultation tool on a cytological laboratory's daily routine has been suggested for several decades. In addition to the use of high-resolution thyroid ultrasonography and fine-needle aspiration cytology, a further reduction of the number of unnecessary thyroidectomies can be achieved through the access to such techniques. Despite the evident advantages, artificial intelligence applications hardly ever find their way to end-users due to the specialized knowledge necessary for designing and using them, as well as the users' unfamiliarity with the required technology. The authors aimed to design an easy-to-use online platform (CytoNet) that gives access to a learning vector quantizer neural network (LVQ NN) that discriminates benign from malignant thyroid lesions to users (medical doctors) with no specialized technical background on artificial intelligence.
Cloud computing has quickly emerged as an exciting new paradigm providing models of computing and services. Via cloud computing technology, bioinformatics tools can be made available as services to anyone, anywhere, and via any device. Large bio-datasets, highly complex algorithms, computing power demanding analysis methods, and the sudden need for hardware and computational resources provide an ideal environment for large-scale bio-data analysis for cloud computing. Cloud computing is already applied in the fields of biology and biochemistry, via numerous paradigms providing novel ideas stimulating future research. The concept of BioCloud has rapidly emerged with applications related to genomics, drug design, biology tools on the cloud, bio-databases, cloud bio-computing, and numerous applications related to biology and biochemistry. In this chapter, the authors present research results related to biology-related laboratories (BioLabs) as well as potential applications for the everyday clinical routine.
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