Oculomics is the science of analyzing ocular data to identify, diagnose, and manage systemic disease. This article focuses on prescreening, its use with retinal images analyzed by artificial intelligence (AI), to identify ocular or systemic disease or potential disease in asymptomatic individuals. The implementation of prescreening in a coordinated care system, defined as Healthcare From the Eye prescreening, has the potential to improve access, affordability, equity, quality, and safety of health care on a global level. Stakeholders include physicians, payers, policymakers, regulators and representatives from industry, government, and data privacy sectors. The combination of AI analysis of ocular data with automated technologies that capture images during routine eye examinations enables prescreening of large populations for chronic disease. Retinal images can be acquired during either a routine eye examination or in settings outside of eye care with readily accessible, safe, quick, and noninvasive retinal imaging devices. The outcome of such an examination can then be digitally communicated across relevant stakeholders in a coordinated fashion to direct a patient to screening and monitoring services. Such an approach offers the opportunity to transform health care delivery and improve early disease detection, improve access to care, enhance equity especially in rural and underserved communities, and reduce costs. With effective implementation and collaboration among key stakeholders, this approach has the potential to contribute to an equitable and effective health care system.

Optical Coherence Tomography (OCT) is a critical imaging modality for diagnosing ocular and systemic conditions, yet its accessibility is hindered by the need for specialized expertise and high computational demands. To address these challenges, we introduce ChatOCT, an offline-capable, domain-adaptive clinical decision support system (CDSS) that integrates structured expert Q&A generation, OCT-specific knowledge injection, and activation-aware model compression. Unlike existing systems, ChatOCT functions without internet access, making it suitable for low-resource environments. ChatOCT is built upon LLaMA-2-7B, incorporating domain-specific knowledge from PubMed and OCT News through a two-stage training process: (1) knowledge injection for OCT-specific expertise and (2) Q&A instruction tuning for structured, interactive diagnostic reasoning. To ensure feasibility in offline environments, we apply activation-aware weight quantization, reducing GPU memory usage to ~ 4.74 GB, enabling deployment on standard OCT hardware. A novel expert answer generation framework mitigates hallucinations by structuring responses in a multi-step process, ensuring accuracy and interpretability. ChatOCT outperforms state-of-the-art baselines such as LLaMA-2, PMC-LLaMA-13B, and ChatDoctor by 10-15 points in coherence, relevance, and clinical utility, while reducing GPU memory requirements by 79%, while maintaining real-time responsiveness (~ 20 ms inference time). Expert ophthalmologists rated ChatOCT's outputs as clinically actionable and aligned with real-world decision-making needs, confirming its potential to assist frontline healthcare providers. ChatOCT represents an innovative offline clinical decision support system for optical coherence tomography (OCT) that runs entirely on local embedded hardware, enabling real-time analysis in resource-limited settings without internet connectivity. By offering a scalable, generalizable pipeline that integrates knowledge injection, instruction tuning, and model compression, ChatOCT provides a blueprint for next-generation, resource-efficient clinical AI solutions across multiple medical domains.

The translation of imaging Mueller polarimetry to clinical practice is often hindered by large footprint and relatively slow acquisition speed of the existing instruments. Using polarization-sensitive camera as a detector may reduce instrument dimensions and allow data streaming at video rate. However, only the first three rows of a complete 4×4 Mueller matrix can be measured. To overcome this hurdle we developed a machine learning approach using sequential neural network algorithm for the reconstruction of missing elements of a Mueller matrix from the measured elements of the first three rows. The algorithm was trained and tested on the dataset of polarimetric images of various excised human tissues (uterine cervix, colon, skin, brain) acquired with two different imaging Mueller polarimeters operating in either reflection (wide-field imaging system) or transmission (microscope) configurations at different wavelengths of 550 nm and 385 nm, respectively. Reconstruction performance was evaluated using various error metrics, all of which confirmed low error values. The reconstruction of full images of the fourth row of Mueller matrix with GPU parallelization and increasing batch size took less than 50 milliseconds. It suggests that a machine learning approach with parallel processing of all image pixels combined with the partial Mueller polarimeter operating at video rate can effectively substitute for the complete Mueller polarimeter and produce accurate maps of depolarization, linear retardance and orientation of the optical axis of biological tissues, which can be used for medical diagnosis in clinical settings.

Currently, plaque segmentation in Optical Coherence Tomography (OCT) images of coronary arteries is primarily carried out manually by physicians, and the accuracy of existing automatic segmentation techniques needs further improvement. To furnish efficient and precise decision support, automated detection of plaques in coronary OCT images holds paramount importance. For addressing these challenges, we propose a novel deep learning algorithm featuring Dense Atrous Convolution (DAC) and attention mechanism to realize high-precision segmentation and classification of Coronary artery plaques. Then, a relatively well-established dataset covering 760 original images, expanded to 8,000 using data enhancement. This dataset serves as a significant resource for future research endeavors. The experimental results demonstrate that the dice coefficients of calcified, fibrous, and lipid plaques are 0.913, 0.900, and 0.879, respectively, surpassing those generated by five other conventional medical image segmentation networks. These outcomes strongly attest to the effectiveness and superiority of our proposed algorithm in the task of automatic coronary artery plaque segmentation.