Artificial neural networking
Dotaz
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- MeSH
- kognice MeSH
- lidé MeSH
- neuronové sítě MeSH
- neuroplasticita MeSH
- percepce MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected exampl
Úvod: Umělé neuronové sítě se stávají důležitou technologií při analýze dat a jejich vliv začíná prostupovat i do oblasti medicíny. Naše pracoviště se dlouhodobě věnuje experimentální chirurgii, na to navazuje náš zájem o pokrok v ostatních oblastech moderních technologií a tím i umělých neuronových sítí. V rámci aktuálního čísla chceme prozkoumat i tento aspekt technického pokroku. Hlavním cílem je kritické zhodnocení silných i slabých stránek technologie umělých neuronových sítí s ohledem na využití v klinické a experimentální chirurgii. Metody: V článku je věnována pozornost in-silico modelování a zejména pak možnostem neuronových sítí s ohledem na zpracování obrazových dat v medicíně. V textu je krátce shrnut historický vývoj hlubokého učení neuronových sítí a základní principy jejich fungování. Dále je představena taxonomie základních řešených úloh. Zmíněny jsou i možné problémy při učení i s možnostmi jejich řešení. Výsledky: Článek poukazuje na rozličné možnosti umělých neuronových sítí v biologických aplikacích. Na řadě biomedicínských aplikací umělých neuronových sítí popisuje rozdělení a princip základních úloh strojového učení a hlubokého učení – klasifikace, detekce a segmentace. Závěr: Aplikace metod umělých neuronových sítí mají v medicíně a chirurgii značný potenciál. Obcházejí potřebu zdlouhavého subjektivního nastavování parametrů znalostním inženýrem, neboť se učí přímo z dat. Při využití nevhodně vyváženého datasetu však může docházet k neočekávaným, avšak zpětně vysvětlitelným chybám. Řešení představuje vytvoření dostatečně bohatého datasetu pro učení a ověření funkce.
Introduction: Artificial neural networks are becoming an essential technology in data analysis, and their influence is starting to permeate the field of medicine. Experimental surgery has been a long-term subject of study of our lab; this is naturally reflected in our interest in other areas of modern technologies including artificial neural networks and their advancements. In the current issue, we would like to explore this aspect of technical progress. The main goal is to critically evaluate the strengths and weaknesses of artificial neural network technology concerning its use in clinical and experimental surgery. Methods: The article is focused on in-silico modeling, particularly on the potential of neural networks in terms of image data processing in medicine. The text briefly summarizes the historical development of deep learning neural networks and their basic principles. Furthermore, basic taxonomy tasks are presented. Finally, potential learning problems and possible solutions are also mentioned. Results: The article points out various possible uses of artificial neural networks in biological applications. Several biomedical applications of artificial neural networks are used to describe the division and principles of the most common tasks of machine learning and deep learning such as classification, detection, and segmentation. Conclusion: The application of artificial neural network methods in medicine and surgery offers a considerable potential; by learning directly from the data, they make it possible to avoid lengthy and subjective setting of parameters by an expert engineer. Nevertheless, the use of an unbalanced dataset can lead to unexpected, although traceable errors. The solution is to collect a dataset large enough to enable both learning and verification of proper functionality.
- MeSH
- deep learning MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítačové zpracování obrazu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.
- MeSH
- elektroencefalografie metody MeSH
- lidé MeSH
- neuronové sítě MeSH
- polysomnografie metody MeSH
- spánek fyziologie MeSH
- stadia spánku fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
The artificial neural networks (ANN) are very often applied in many areas of toxicology for the solving of complex problems, such as the prediction of chemical compound properties and quantitative structure-activity relationship. The aim of this contribution is to give the basic knowledge about conception of ANN, theirs division and finally, the typical application of ANN will be discussed. Due to the diversity of architectures and adaptation algorithms, the ANNs are used in the broad spectrum of applications from the environmental processes modeling, through the optimization to quantitative structure-activity relationship (QSAR) methods. In addition, especially ANNs with Kohonen learning are very effective classification tool. The ANNs are mostly applied in cases, where the commonly used methods does not work.
Examination of semen characteristics is routinely performed for fertility status investigation of the male partner of an infertile couple as well as for evaluation of the sperm donor candidate. A useful tool for preliminary assessment of semen characteristics might be an artificial neural network. Thus, the aim of the present study was to construct an artificial neural network, which could be used for predicting the result of semen analysis based on the basic questionnaire data. On the basis of eleven survey questions two models of artificial neural networks to predict semen parameters were developed. The first model aims to predict the overall performance and profile of semen. The second network was developed to predict the concentration of sperm. The network to evaluate sperm concentration proved to be the most efficient. 92.93% of the patients in the learning process were properly qualified for the group with a correct or incorrect result, while the result for the test set was 85.71%. This study suggests that an artificial neural network based on eleven survey questions might be a valuable tool for preliminary evaluation and prediction of the semen profile.
- MeSH
- analýza spermatu * metody přístrojové vybavení MeSH
- lidé MeSH
- motilita spermií MeSH
- mužská infertilita MeSH
- neuronové sítě * MeSH
- počet spermií metody přístrojové vybavení MeSH
- průzkumy a dotazníky MeSH
- sperma * MeSH
- spermabanky MeSH
- spermie * abnormality růst a vývoj ultrastruktura MeSH
- Check Tag
- lidé MeSH
