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Abstract Drug-related errors are a leading cause of preventable patient harm in the clinical setting. We present the first wearable camera system to automatically detect potential errors, prior to medication delivery. We demonstrate that using deep learning algorithms, our system can detect and classify drug labels on syringes and vials in drug preparation events recorded in real-world operating rooms. We created a first-of-its-kind large-scale video dataset from head-mounted cameras comprising 4K footage across 13 anesthesiology providers, 2 hospitals and 17 operating rooms over 55 days. The system was evaluated on 418 drug draw events in routine patient care and a controlled environment and achieved 99.6% sensitivity and 98.8% specificity at detecting vial swap errors. These results suggest that our wearable camera system has the potential to provide a secondary check when a medication is selected for a patient, and a chance to intervene before a potential medical error.

Abstract Imagine being in a crowded room with a cacophony of speakers and having the ability to focus on or remove speech from a specific 2D region. This would require understanding and manipulating an acoustic scene, isolating each speaker, and associating a 2D spatial context with each constituent speech. However, separating speech from a large number of concurrent speakers in a room into individual streams and identifying their precise 2D locations is challenging, even for the human brain. Here, we present the first acoustic swarm that demonstrates cooperative navigation with centimeter-resolution using sound, eliminating the need for cameras or external infrastructure. Our acoustic swarm forms a self-distributing wireless microphone array, which, along with our attention-based neural network framework, lets us separate and localize concurrent human speakers in the 2D space, enabling speech zones. Our evaluations showed that the acoustic swarm could localize and separate 3-5 concurrent speech sources in real-world unseen reverberant environments with median and 90-percentile 2D errors of 15 cm and 50 cm, respectively. Our system enables applications like mute zones (parts of the room where sounds are muted), active zones (regions where sounds are captured), multi-conversation separation and location-aware interaction.

Abstract Background Millions of catheters for invasive arterial pressure monitoring are placed annually in intensive care units, emergency rooms, and operating rooms to guide medical treatment decision-making. Accurate assessment of arterial blood pressure requires an IV pole-attached pressure transducer placed at the same height as a reference point on the patient’s body, typically, the heart. Every time a patient moves, or the bed is adjusted, a nurse or physician must adjust the height of the pressure transducer. There are no alarms to indicate a discrepancy between the patient and transducer height, leading to inaccurate blood pressure measurements. Methods We present a low-power wireless wearable tracking device that uses inaudible acoustic signals emitted from a speaker array to automatically compute height changes and correct the mean arterial blood pressure. Performance of this device was tested in 26 patients with arterial lines in place. Results Our system calculates the mean arterial pressure with a bias of 0.19, inter-class correlation coefficients of 0.959 and a median difference of 1.6 mmHg when compared to clinical invasive arterial measurements. Conclusions Given the increased workload demands on nurses and physicians, our proof-of concept technology may improve accuracy of pressure measurements and reduce the task burden for medical staff by automating a task that previously required manual manipulation and close patient surveillance.

Background: Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored. Objective: The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data. Methods: We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0–2 days, ±3–7 days, and ±8–30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively. Results: We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759–0.760), sensitivity of 0.703 (95% CI 0.700–0.705), specificity of 0.684 (95% CI 0.678–0.685), and accuracy of 69.4% (95% CI 0.692–0.700). Model performance was better on ±0–2 day samples (sensitivity 0.711; 95% CI 0.709–0.713) and worse on the ±8–30 day window (sensitivity 0.688; 95% CI 0.685–0.690), with performance on the ±3–7 day window falling in between (sensitivity 0.708; 95% CI 0.704–0.710). Conclusion: Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

Chan et al. develop an open-source smartphone-based tympanometer to evaluate middle ear function. The authors test their device in a group of paediatric patients at an audiology clinic and report a high level of agreement in audiologists’ classification of tympanograms produced using this device compared with a commercial device.

Therapy with anticoagulants requires frequent monitoring. Here the authors describe a proof-of-concept study of a simple and affordable blood clot test that uses a smartphone’s vibration motor and camera to track micro-movements in a single drop of blood.

Abstract Overdoses from non-medical use of opioids can lead to hypoxemic/hypercarbic respiratory failure, cardiac arrest, and death when left untreated. Opioid toxicity is readily reversed with naloxone, a competitive antagonist that can restore respiration. However, there remains a critical need for technologies to administer naloxone in the event of unwitnessed overdose events. We report a closed-loop wearable injector system that measures respiration and apneic motion associated with an opioid overdose event using a pair of on-body accelerometers, and administers naloxone subcutaneously upon detection of an apnea. Our proof-of-concept system has been evaluated in two environments: (i) an approved supervised injection facility (SIF) where people self-inject opioids under medical supervision and (ii) a hospital environment where we simulate opioid-induced apneas in healthy participants. In the SIF (n = 25), our system identified breathing rate and post-injection respiratory depression accurately when compared to a respiratory belt. In the hospital, our algorithm identified simulated apneic events and successfully injected participants with 1.2 mg of naloxone. Naloxone delivery was verified by intravenous blood draw post-injection for all participants. A closed-loop naloxone injector system has the potential to complement existing evidence-based harm reduction strategies and, in the absence of bystanders, help make opioid toxicity events functionally witnessed and in turn more likely to be successfully resuscitated.

Anran Wang et al. present a contact-free method of monitoring individual heart beats by converting smart-speakers into active sonar systems. Their approach is capable of measuring heart rhythms with high accuracy in both healthy participants and hospitalized patients, and may be a useful healthcare tool for remote diagnosis or patient monitoring.