An Additive Manufacturing Technique for the Facile and Rapid Fabrication of Hydrogel-based Micromachines with Magnetically Responsive Components.

Polyethylene glycol (PEG)-based hydrogels are biocompatible hydrogels that have been approved for use in humans by the FDA. Typical PEG-based hydrogels have simple monolithic architectures and often function as scaffolding materials for tissue engineering applications. More sophisticated structures typically take a long time to fabricate and do not contain moving components. This protocol describes a photolithography method that allows for facile and rapid microfabrication of PEG structures and devices. This strategy involves an in-house developed fabrication stage that allows for the rapid fabrication of 3D structures by building upwards in a layer-by-layer fashion. Independent moving components can also be aligned and assembled onto support structures to form integrated devices. These independent components are doped with superparamagnetic iron oxide nanoparticles that are sensitive to magnetic actuation. In this manner, the fabricated devices can be actuated using external magnets to yield movement of the components within. Hence, this technique allows for the fabrication of sophisticated MEMS-like devices (micromachines) that are composed entirely out of a biocompatible hydrogel, able to function without an onboard power source, and respond to a contact-less method of actuation. This manuscript describes the fabrication of both the fabrication set-up as well as the step-by-step method for the microfabrication of these hydrogels-based MEMS-like devices.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

OBJECTIVE: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms. METHODS: We trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison. RESULTS: In the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924-0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%). CONCLUSIONS: In this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.