Nature Biomedical Engineering最新文献

The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets of three-dimensional (3D) scans for model training. Here we report a deep-learning model pre-trained on 2D scans (for which annotated data are relatively abundant) that accurately predicts disease-risk factors from 3D medical-scan modalities. The model, which we named SLIViT (for ‘slice integration by vision transformer’), preprocesses a given volumetric scan into 2D images, extracts their feature map and integrates it into a single prediction. We evaluated the model in eight different learning tasks, including classification and regression for six datasets involving four volumetric imaging modalities (computed tomography, magnetic resonance imaging, optical coherence tomography and ultrasound). SLIViT consistently outperformed domain-specific state-of-the-art models and was typically as accurate as clinical specialists who had spent considerable time manually annotating the analysed scans. Automating diagnosis tasks involving volumetric scans may save valuable clinician hours, reduce data acquisition costs and duration, and help expedite medical research and clinical applications.

The clinical assessment of microvascular pathologies (in diabetes and in inflammatory skin diseases, for example) requires the visualization of superficial vascular anatomy. Photoacoustic tomography (PAT) scanners based on an all-optical Fabry–Perot ultrasound sensor can provide highly detailed 3D microvascular images, but minutes-long acquisition times have precluded their clinical use. Here we show that scan times can be reduced to a few seconds and even hundreds of milliseconds by parallelizing the optical architecture of the sensor readout, by using excitation lasers with high pulse-repetition frequencies and by exploiting compressed sensing. A PAT scanner with such fast acquisition minimizes motion-related artefacts and allows for the volumetric visualization of individual arterioles, venules, venous valves and millimetre-scale arteries and veins to depths approaching 15 mm, as well as for dynamic 3D images of time-varying tissue perfusion and other haemodynamic events. In exploratory case studies, we used the scanner to visualize and quantify microvascular changes associated with peripheral vascular disease, skin inflammation and rheumatoid arthritis. Fast all-optical PAT may prove useful in cardiovascular medicine, oncology, dermatology and rheumatology.

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) lack nanoscale structures essential for efficient excitation–contraction coupling. Such nanostructures, known as dyads, are frequently disrupted in heart failure. Here we show that the reduced expression of cardiomyopathy-associated 5 (CMYA5), a master protein that establishes dyads, contributes to dyad disorganization in heart failure and to impaired dyad assembly in hiPSC-CMs, and that a miniaturized form of CMYA5 suitable for delivery via an adeno-associated virus substantially improved dyad architecture and normalized cardiac function under pressure overload. In hiPSC-CMs, the miniaturized form of CMYA5 increased contractile forces, improved Ca²⁺ handling and enhanced the alignment of sarcomere Z-lines with ryanodine receptor 2, a protein that mediates the sarcoplasmic release of stored Ca²⁺. Our findings clarify the mechanisms responsible for impaired dyad structure in diseased cardiomyocytes, and suggest strategies for promoting dyad assembly and stability in heart disease and during the derivation of hiPSC-CMs.