Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations.

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data.

Spontaneous and salt stress-induced molecular instability in the progeny of MSH7 deficient Arabidopsis thaliana plants.

The MSH7 protein is a binding partner of MSH2 forming the MutSγ complex. This complex contributes to the plant mismatch repair (MMR) system by recognizing DNA base-base mismatches. Here, we evaluated the impact of MSH7 on genetic diversity of the tenth generation (G) of wild type and MSH7 deficient Arabidopsis thaliana plants before and after two days exposure to 100 mM NaCl.

Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022.

Non-invasive physiological assessment of intermediate coronary stenoses from plain angiography through artificial intelligence: the STARFLOW system.

Background: Despite evidence supporting use of fractional flow reserve (FFR) and instantaneous waves-free ratio (iFR) to improve outcome of patients undergoing coronary angiography (CA) and percutaneous coronary intervention, such techniques are still underused in clinical practice due to economic and logistic issues.

Objectives: We aimed to develop an artificial intelligence (AI)-based application to compute FFR and iFR from plain CA.

Methods And Results: Consecutive patients performing FFR or iFR or both were enrolled.

Wake-Sleep Consolidated Learning.

We propose wake-sleep consolidated learning (WSCL), a learning strategy leveraging complementary learning system (CLS) theory and the wake-sleep phases of the human brain to improve the performance of deep neural networks (DNNs) for visual classification tasks in continual learning (CL) settings. Our method learns continually via the synchronization between distinct wake and sleep phases. During the wake phase, the model is exposed to sensory input and adapts its representations, ensuring stability through a dynamic parameter freezing mechanism and storing episodic memories in a short-term temporary memory (similar to what happens in the hippocampus).