The use of artificial intelligence methods in pathology

Abstract

Souasn patologie prochz zsadn promnou. Z tradinho morfologickho oboru se stv obor na pomez klasick histopatologie, molekulrn biologie a bioinformatiky. Rozvoj na poli vpoetn techniky a uml inteligence (AI) umouje zpracovvat nejen data, kter generuj molekulrn patologick analyztory, ale i mikroskopick obrazy zskan pomoc vysokokapacitnch skener. Digitalizovan mikroskopick preparty, tzv. whole slide images (WSI), je tak mon dle podrobovat algoritmickmu zpracovn pomoc celho spektra nstroj strojovho uen, mimo jin tak hlubokho uen zaloenho na umlch neuronovch stch. Prv nstroje zaloen na hlubokm uen otevely nov monosti v oblasti diagnostiky. Ukazuje se, e jsou schopn detekovat ndory i metastzy v lymfatickch uzlinch, rychle odhadovat jejich grade, a dokonce predikovat molekulrn patologick zmny ndorovch onemocnn. Podobn jako v ostatnch medicnskch oborech ani v patologii nen digitalizace bez svch skal. Vroba histologickho prepartu je pedevm run prac, a tak je nutn klst velk draz na jej kvalitu, na n je pak zvisl sprvn fungovn skener a nsledn i digitln patologickch nstroj. Mimo tyto pro patologii specifick limitace narme i na pekky legislativn, ekonomick a technologick, jako je archivace a zpracovn ohromnch objem dat v du stovek a tisc terabyt ron. Digitln a vpoetn patologie pedstavuj dynamicky se vyvjejc diagnostick obor s vraznm dopadem na budoucnost ndorov diagnostiky a personalizovan medicny. Modern pathology is currently undergoing a fundamental transformation. It is evolving from a purely morphological discipline to a n integrated field that combines histopathology with molecular biology and bioinformatics. Rapid advances in computational technology and artificial intelligence (AI) have enabled the processing of not only data generated by molecular pathology analyzers, but also of microscopic images, produced by high-capacity slide scanners. These digitized microscopic slides, also known as whole slide images (WSI) , can then be algorithmically processed using a wide range of machine learning tools, including deep learning, a subset of machine learning based on artificial neural networks. These deep learning-based approaches have opened new possibilities in diagnostics. They have demonstrated the ability to, among others, detect tumors and lymph node metastases, estimate tumor grade, and even predict molecular alterations. As in other disciplines of medicine, digitization in pathology is not without its challenges. The preparation of histological slides remains largely a manual process making it essential to maintain high-quality standards, as scanners and digital pathology tools rely heavily on clear, artefact-free slides to function properly In addition to these pathology-specific limitations, broader economic, legal, and technical challenges must also be addressed, including the storage and analysis of massive volumes of data, which may reach hundreds or even thousands of terabytes annually. Digital and computational pathology are rapidly advancing fields with profound implications for the future of cancer diagnostics and personalized medicine.

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