An official website of the United States government
Cardiopulmonary ailments are a major cause of mortality. Stethoscopes are one of the most important tools that healthcare professionals use to screen patients for a variety of ailments, especially those related to the heart and lungs. Despite the growth of digital stethoscopes on the market, it takes years of training to properly use stethoscopes to listen for abnormal sounds within the body. In this demonstration, we present an intelligent stethoscope platform that makes stethoscopes more accessible to the general population. Our platform utilizes augmented reality (AR) to provide real-time guidance on where to properly place the stethoscope on the body, enabling the general population to screen themselves for ailments.
more »
« less
ARSteth: Enabling Home Self-Screening with AR-Assisted Intelligent Stethoscopes
The stethoscope is one of the most important diagnostic tools used by healthcare professionals, through a process called auscultation, to screen patients for abnormalities of the heart and lungs. While there are digital stethoscopes on the market which ease this process, it still takes years of training to properly use these devices to listen for abnormal sounds within the body. We present ARSteth, an intelligent stethoscope platform that improves the accessibility of stethoscopes for the general population, allowing anyone to perform auscultation in the comfort of their own homes. Our platform utilizes a combination of augmented reality (AR), acoustic intelligence, and human-machine interaction to dynamically guide users on where to place the stethoscope on different parts of the body (auscultation points), through visual and audio cues. Through user studies, we show that ARSteth, on average, can guide users within 13.2 mm from optimal auscultation points marked by licensed physicians in 13.09 seconds for each auscultation point. By guiding users towards more effective auscultation points, make preventative health screening more accessible and effective for everyone we are able to achieve higher confidence on classifying heart murmurs.
more »
« less
- PAR ID:
- 10416022
- Date Published:
- Journal Name:
- Proceedings of the 22nd International Conference on Information Processing in Sensor Networks
- Page Range / eLocation ID:
- 205 to 218
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Longitudinal fetal health monitoring is essential for high-risk pregnancies. Heart rate and heart rate variability are prime indicators of fetal health. In this work, we implemented two neural network architectures for heartbeat detection on a set of fetal phonocardiogram signals captured using fetal Doppler and a digital stethoscope. We test the efficacy of these networks using the raw signals and the hand-crafted energy from the signal. The results show a Convolutional Neural Network is the most efficient at identifying the S1 waveforms in a heartbeat, and its performance is improved when using the energy of the Doppler signals. We further discuss issues, such as low Signal-to-Noise Ratios (SNR), present in the training of a model based on the stethoscope signals. Finally, we show that we can improve the SNR, and subsequently the performance of the stethoscope, by matching the energy from the stethoscope to that of the Doppler signal.more » « less
-
As content moderation becomes a central aspect of all social media platforms and online communities, interest has grown in how to make moderation decisions contestable. On social media platforms where individual communities moderate their own activities, the responsibility to address user appeals falls on volunteers from within the community. While there is a growing body of work devoted to understanding and supporting the volunteer moderators' workload, little is known about their practice of handling user appeals. Through a collaborative and iterative design process with Reddit moderators, we found that moderators spend considerable effort in investigating user ban appeals and desired to directly engage with users and retain their agency over each decision. To fulfill their needs, we designed and built AppealMod, a system that induces friction in the appeals process by asking users to provide additional information before their appeals are reviewed by human moderators. In addition to giving moderators more information, we expected the friction in the appeal process would lead to a selection effect among users, with many insincere and toxic appeals being abandoned before getting any attention from human moderators. To evaluate our system, we conducted a randomized field experiment in a Reddit community of over 29 million users that lasted for four months. As a result of the selection effect, moderators viewed only 30% of initial appeals and less than 10% of the toxically worded appeals; yet they granted roughly the same number of appeals when compared with the control group. Overall, our system is effective at reducing moderator workload and minimizing their exposure to toxic content while honoring their preference for direct engagement and agency in appeals.more » « less
-
Abstract While the human body has many different examples of perfusable structures with complex geometries, biofabrication methods to replicate this complexity are still lacking. Specifically, the fabrication of self‐supporting, branched networks with multiple channel diameters is particularly challenging. Herein, the Gelation of Uniform Interfacial Diffusant in Embedded 3D Printing (GUIDE‐3DP) approach for constructing perfusable networks of interconnected channels with precise control over branching geometries and vessel sizes is presented. To achieve user‐specified channel dimensions, this technique leverages the predictable diffusion of cross‐linking reaction‐initiators released from sacrificial inks printed within a hydrogel precursor. The versatility of GUIDE‐3DP to be adapted for use with diverse physicochemical cross‐linking mechanisms is demonstrated by designing seven printable material systems. Importantly, GUIDE‐3DP allows for the independent tunability of both the inner and outer diameters of the printed channels and the ability to fabricate seamless junctions at branch points. This 3D bioprinting platform is uniquely suited for fabricating lumenized structures with complex shapes characteristic of multiple hollow vessels throughout the body. As an exemplary application, the fabrication of vasculature‐like networks lined with endothelial cells is demonstrated. GUIDE‐3DP represents an important advance toward the fabrication of self‐supporting, physiologically relevant networks with intricate and perfusable geometries.more » « less
-
Recent platforms utilize ML task performance metrics, not metadata keywords, to search large data corpus. Requesters provide an initial dataset, and the platform searches for additional datasets that augment---join or union---requester's dataset to most improve the model (e.g., linear regression) performance. Although effective, current task-based data searches are stymied by (1) high latency which deters users, (2) privacy concerns for regulatory standards, and (3) low data quality which provides low utility. We introduce Mileena, a fast, private, and high-quality task-based dataset search platform. At its heart, Mileena is built on pre-computed semi-ring sketches for efficient ML training and evaluation. Based on semi-ring, we develop a novel Factorized Privacy Mechanism that makes the search differentially private and scales to arbitrary corpus sizes and numbers of requests without major quality degradation. We also demonstrate the early promise in using LLM-based agents for automatic data transformation and applying semi-rings to support causal discovery and treatment effect estimation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3583120.3586962
