Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

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In cervical biopsies, for diagnosis of Human Papilloma Virus (HPV) related conditions, the immunohistochemical staining for p16 has a diagnostic value only if diffusely and strongly positive, pattern named “block-like”. “Weak and/or focal (w/f) p16 expression” is commonly considered nonspecific. In our previous study, we demonstrated the presence of high-risk HPV (hrHPV) DNA by LiPa method in biopsies showing w/f p16 positivity. The aim of the present study was to investigate the presence of hrHPV-DNA by CISH in the areas showing w/f p16 expression. We assessed the presence of hrHPV16, 18, 31, 33, 51 by CISH in a group of 20 cervical biopsies showing w/f p16 expression, some with increased Ki67, and in 10 cases of block-like expression, employed as control. The immunohistochemical p16 expression was also assessed by digital pathology. hrHPV-CISH nuclear positivity was encountered in 12/20 cases of w/f p16 expression (60%). Different patterns of nuclear positivity were identified, classified as punctate, diffuse and mixed, with different epithelial distributions. Our results, albeit in a limited casuistry, show the presence of HPV in an integrated status highlighted by CISH in w/f p16 positive cases. This could suggest the necessity of a careful follow-up of the patients with “weak” and/or “focal” immunohistochemical patterns of p16, mainly in cases of increased Ki67 cell proliferation index, supplemented with molecular biology examinations.

(1) Background: Nonalcoholic Steatohepatitis/Nonalcoholic Fatty Liver Disease (NASH/NAFLD) is the most recurrent chronic liver disease. NASH could present with a cholestatic (C) or hepatic (H) pattern of damage. Recently, we observed that increased Epithelial Cell Adhesion Molecule (EpCAM) expression was the main immunohistochemical feature to distinguish C from H pattern in NASH. (2) Methods: In the present study, we used digital pathology to compare the quantitative results of digital image analysis by QuPath software (Q-results), with the semi-quantitative results of observer assessment (S-results) for cytokeratin 7 and 19, (CK7, CK19) as well as EpCAM expression. Patients were classified into H or C group on the basis of the ratio between alanine transaminase (ALT) and alkaline phosphatase (ALP) values, using the “R-ratio formula”. (3) Results: Q- and S-results showed a significant correlation for all markers (p < 0.05). Q-EpCAM expression was significantly higher in the C group than in the H group (p < 0.05). Importantly ALP, an indicator of hepatobiliary disorder, was the only biochemical parameter significantly correlated with Q-EpCAM. Instead, Q-CK7, but not Q-CK19, correlated only with γGlutamyl-Transferase (γGT). Of note, Stage 4 fibrosis correlated with Q-EpCAM, Q-CK19, and ALP but not with γGT or ALT. Conclusions: Image analysis confirms the relation between cholestatic-like pattern, associated with a worse prognosis, with increased ALP values, EpCAM positive biliary metaplasia, and advanced fibrosis. These preliminary data could be useful for the implementation of AI algorithms for the assessment of cholestatic NASH.