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Digital pathology, image analysis, and artificial intelligence in liver disease

Advances in digital pathology, image analysis, and artificial intelligence (AI) are rapidly transforming how pathologists and researchers interact with tissue samples and enable the development of diagnostic tools that harness high-resolution whole-slide images; these advances are in turn creating new opportunities for research, education, and routine clinical care globally. Liver disease is no exception, and digital pathology and AI have many applications in the diagnosis of liver cancer and liver diseases and…

The Lancet Digital Health · Health

Digital pathology, image analysis, and artificial intelligence in liver disease

Evidence-backed gain

Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes

Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment. Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases. Fun…

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Evidence-backed problem

Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation

Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporation of computational technologies within both academic and pharmaceutical environments. This evolution is marked by a significant accumulation of data pertaining to detailed three-dimensional structural information, ligand properties, and their interactions with therapeutic targets. The augmentation of computational capabilities and the accessibility of extensive chemical libraries containing billions of d…

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Evidence-backed gain

Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients

Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (pITP) from connective tissue disease-related thrombocytopenia (CTD-TP) in children presenting with thrombocytopenia. Method A retrospective study was conducted on 387 newly diagnosed children with thrombocytopenia. All patients were clinically diagnosed and divided into a training set and a test set in a 7:3 ratio. Six machine learning algorithms, including XGboost, RF, SVM, LR, GBDT, BPNN, were used to e…

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Evidence-backed problem

SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

Multimodal artificial intelligence, albeit showing great potential in computational pathology, remains limited to isolated patch-level interpretation and often fails to analyze gigapixel-scale whole-slide images (WSIs) essential for clinical utility. Here we present SlideChat, a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types. SlideChat integrates patch-level and slide-level pathology encoders with a pretrained large language model. Using 274,23…

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Evidence-backed problem

Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging

Artificial intelligence (AI) has rapidly transformed cardiac CT, extending its clinical utility from coronary CT angiography (CCTA) to CT myocardial perfusion imaging (CT-MPI). This review outlines the current advances in and future perspectives on AI-aided cardiac CT across anatomical, functional, and prognostic dimensions. In CCTA, AI can automate calcium scoring, vessel segmentation, and plaque characterization, markedly improving workflow efficiency and reproducibility. Deep-learning models can allow accurat…

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Evidence-backed gain

A polyline searching-driven evolutionary AI for disease detection of medical imaging data

Objective. Accurate Ultrasound (US) prostate cancer (PCa) segmentation images hold significant value for organ interventional guidance and clinical disease diagnosis. However, this task still poses substantial challenges. The main obstacles include blurred or incomplete boundaries separating PCa from adjacent soft tissues, shadow artifacts inherent to ultrasound imaging, and drastic inter-patient variations in organ morphological shapes. Approach . To address these issues, our method introduces a novel coarse-to…

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Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations

Background There is increasing recognition that trauma exposure and related psychiatric consequences predict cardiovascular disease risk. However, most research has specifically examined post-traumatic stress disorder-despite evidence that a range of psychiatric disorders may follow trauma. We applied machine learning to identify key post-traumatic psychiatric predictors of incident major adverse cardiac and cerebrovascular events (MACCE) in a population-based cohort. Methods We conducted a case-control study of…

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Post-traumatic Psychopathology and Cardiovascular Risk: A Danish Case-Control Study of Sex-Specific Associations

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study

Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use. This study therefore proposes a highly sensitive artificial intelligence (AI)-assisted diagnostic system for GAS based exclusively on H&E-stained histopathological images. We incl…

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[Artificial intelligence in cardiovascular prevention: a strategic opportunity for risk prediction and early diagnosis]

Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardio…

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Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank

Osteoporosis (OP) is often underdiagnosed, highlighting the need for tools that can both detect existing disease and predict future risk; large-scale plasma proteomics combined with explainable machine learning enables integrated diagnostic and prognostic modeling while prioritizing clinically relevant protein markers. This study aims to develop and validate an explainable plasma proteomics machine-learning framework for osteoporosis diagnosis, future risk prediction, and biomarker discovery. We further tested w…

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Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis

Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While artificial intelligence (AI) shows promise for improving diagnostic capacity, its clinical utility requires rigorous evaluation. Following PRISMA 2020 guidelines, this systematic review evaluated machine and deep learning techniques for HD diagnosis from histo…

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Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors

Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the G1-to-S phase transition, and their dysregulation is a hallmark of numerous malignancies. Despite the clinical success of existing CDK4/6 inhibitors, there remains a persistent need for chemically diverse scaffolds with potent dual-target affinity. In this study, we developed and implemented a virtual screening workflow that synergistically integrates ligand-based machine learning with structure-based molecular docking. By benchmarking multi…

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Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening

Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer screening. This study evaluated an artificial intelligence (AI) pulmonary nodule analysis system in all 361 UK Lung Cancer Screening (UKLS) trial participants who underwent a 3-month follow-up low-dose computed tomography (LDCT) scan. The pulmonary AI independently evaluated these baseline scans using an updated volume threshold (solid component ≥ 100 mm³ per NELSO…

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Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroid carcinoma (IEFVPTC) are diagnostically challenging thyroid neoplasms with overlapping clinical and molecular characteristics. Although artificial intelligence has shown promise for diagnostic support in radiology and histopathology, its application to gross pathology remains unexplored. This study analyzed gross pathology photographs from 87 patients (43 with NIF…

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Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images