A hybrid framework with large language models for rare disease phenotyping.

Purpose: Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports.

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.

Background: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event.

Methods: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database.

Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks.

Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical context. However, the transfer of such segmentation methods to the preclinical research is compounded by the limited number of datasets and lower image resolution.

Multimodal Continual Learning with Sonographer Eye-Tracking in Fetal Ultrasound.

Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a different and difficult challenge due to the tendency of reduction in performance over old classes while adapting to new ones. Controlling such a 'forgetting' is vital for deployed algorithms to evolve with new arrivals of data incrementally.

Hierarchical Class Incremental Learning of Anatomical Structures in Fetal Echocardiography Videos.

This paper proposes an ultrasound video interpretation algorithm that enables novel classes or instances to be added over time, without significantly compromising prediction abilities on prior representations. The motivating application is diagnostic fetal echocardiography analysis. Currently in clinical practice, recording full diagnostic fetal echocardiography is not common.

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations.

Automated Myocardial Wall Motion Classification using Handcrafted Features vs a Deep CNN-based mapping.

Compared to other modalities such as computed tomography or magnetic resonance imaging, the appearance of ultrasound images is highly dependent on the expertise of the sonographer or clinician making the image acquisition, as well as the machine used, making it a challenge to analyze due to the frequent presence of artefacts, missing boundaries, attenuation, shadows, and speckle. In addition, manual contouring of the epicardial and endocardial walls exhibits large inconsistencies and variations as it is strongly dependent on the sonographer's training and expertise. Hence, in this paper we propose a fully automated image analysis framework to ultimately perform wall motion abnormality classification in 2D+T images.

Improving Visual Detection of Wall Motion Abnormality with Echocardiographic Image Enhancing Methods.

Analysis of wall motion abnormality using echocardiography is an established method for detecting myocardial ischemia. We describe a hybrid approach of enhancing 2D+T echo datasets with border detection and Eulerian motion magnification to improve the visual assessment of wall motion. We implemented a local phase-based approach using the monogenic signal and its derived features, either feature asymmetry (FA) or oriented feature symmetry (OFS), to detect boundaries of the heart structure.