Pathology

Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.

To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD), integrating relevant clinical indicators, and employing machine learning techniques to construct a precise model for detecting severe coronary artery stenosis. A retrospective analysis of 710 T2DM patients with NAFLD was conducted at First People's Hospi...

Tisslet tissues-based learning estimation for transcriptomics.

In the context of multi-omics data analytics for various diseases, transcriptome-wide association studies leveraging genetically predicted gene expression hold promise for identifying novel regions linked to complex traits. However, existing methods for multi-tissue gene expression prediction often fail to account for tissue-tissue expression interactions, limiting their accuracy and effectiveness...

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered.

Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological condi...

Label-Free Detection of Biochemical Changes during Cortical Organoid Maturation via Raman Spectroscopy and Machine Learning.

Human cerebral organoids have become valuable tools in neurodevelopment research, holding promise for investigating neurological diseases and reducing...

Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.

Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed a gradually significant public health c...

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.

Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous d...

Minimally invasive modalities for keratinocyte carcinomas-Part I: Diagnostics.

Keratinocyte carcinoma, including basal cell and squamous cell carcinoma, is the most prevalent form of cancer within the United States, with its inci...

Multimodal Artificial Intelligence-Based Virtual Biopsy for Diagnosing Abdominal Lavage Cytology-Positive Gastric Cancer.

Gastric cancer with peritoneal dissemination remains a significant clinical challenge due to its poor prognosis and difficulty in early detection. Thi...

Improving Malaria diagnosis through interpretable customized CNNs architectures.

Malaria, which is spread via female Anopheles mosquitoes and is brought on by the Plasmodium parasite, persists as a serious illness, especially in ar...

Semi-supervised tissue segmentation from histopathological images with consistency regularization and uncertainty estimation.

Pathologists have depended on their visual experience to assess tissue structures in smear images, which was time-consuming, error-prone, and inconsis...

Content-Based Histopathological Image Retrieval.

Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, an ...

Optimizing thermal dose prediction in nanoparticle-mediated photothermal therapy using a convolutional neural network-based model.

Nanoparticle-mediated photothermal therapy (NMPTT) is an up-and-coming targeted cancer treatment. Here, nanoparticles are used to convert near-infrare...

AI-augmented Biophysical modeling in thermoplasmonics for real-time monitoring and diagnosis of human tissue infections.

Identifying tissue infections from the body still poses an unprecedented challenge in society. Conventional diagnostic procedures are time-consuming a...

Towards precision medicine strategies using plasma proteomic profiling for suspected gallbladder cancer: A pilot study.

BACKGROUND & AIMS: Currently, preoperative diagnostic methods that can distinguish cancer from benign disease of the gallbladder are insufficient, and...

Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training.

PURPOSE: This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thy...

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspiciou...

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.

Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therap...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred for a week's history of dysphagia. Upper gastrointes...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Assessment of AI-based computational H&E staining versus chemical H&E staining for primary diagnosis in lymphomas: a brief interim report.

Microscopic review of tissue sections is of foundational importance in pathology, yet the traditional chemistry-based histology laboratory methods are...