Latest AI and machine learning research in pathology for healthcare professionals.
Artificial intelligence-based computer-aided diagnosis (CADx) systems have seen growing adoption in mammography, yet the limited interpretability of their decision-making processes remains a barrier to clinical trust. The present study aimed to investigate whether deep learning classifiers primarily rely on the characteristics of lesions or the surrounding breast tissue through a counterfactual re...
Single-molecule tracking in living cells measures protein diffusivity but requires sparse imaging, limiting high-density mapping. Here we introduce single-molecule localization and diffusivity microscopy (SMLDM), a deep learning-based approach that accurately estimates single-molecule movement tracks and diffusion coefficients directly from single-frame snapshots, eliminating the need for trajecto...
Lipid nanoparticle (LNP) delivery of RNA therapeutics is constrained by poor tissue selectivity and off-target toxicity. Most high-throughput screenin...
The long, tortuous, and tissue-homogeneous structure of the small bowel makes image-based three-dimensional (3D) modeling studies technically complex....
Bloodstain Pattern Analysis (BPA) is a technique used in forensic investigations to recreate violent crime scenes by examining the distribution of blo...
This comprehensive review examines using artificial intelligence (AI) across the diagnostic, therapeutic, and prognostic landscape of bladder cancer. ...
Artificial intelligence (AI) has the potential of reshaping GI oncology by enabling more nuanced interpretation of complex clinical, imaging, and mole...
Cytopathology is the first field of pathology in which artificial intelligence (AI) models were successfully developed and commercialized for routine ...
CONTEXT: Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, an...
SUMMARYPathogen genomics, including whole-genome sequencing (WGS) and clinical metagenomics, is a transformative technology increasingly being impleme...
Gram staining provides rapid microbiological information that may assist in empirical antimicrobial selection; however, the results are often interpre...
Spatial omics technologies, such as mass spectrometry imaging (MSI), can capture biomolecular distributions and their spatial locations directly from ...
The interpretation and classification of nonsynonymous single nucleotide variants (nsSNVs) remains a significant challenge in clinical genomics partic...
Recent advances in spatial transcriptomics (ST) have generated an expanding collection of heterogeneous datasets, offering unprecedented opportunities...
One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the ...
Understanding the spatial mechanisms of multi-enzyme lignocellulose deconstruction is hindered by the lack of spatial quantification and nondestructiv...
MOTIVATION: Spatial transcriptomics techniques capture gene expression data and spatial coordinates, while simultaneously correlating them with tissue...
Alzheimer's disease neuropathological changes (ADNC)-operationalized with semi-quantitative parameters-represent the consensus-based gold standard for...
BACKGROUND: Optimization of biotechnological processes is traditionally limited by time-consuming trial-and-error approaches and the complexity of sim...
BACKGROUND: The inference of molecular information from hematoxylin-eosin (HE) specimens may reduce the ancillary testing burden in digital pathology....