Real-world evaluation of the Stemoscope electronic tele-auscultation system

Fan, Muge; Wang, Qiuli; Liu, Jiaqi; Su, Lingyun; Wang, Bingjian; Xu, Hai; Li, Qing; Zhang, Zhi

doi:10.1186/s12938-022-01032-4

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…The development of such algorithms will assist the veterinary profession on a global scale and aid in creating algorithms for other species. This will ultimately result in increased telemedicine capabilities to assist rural, remote and even third-world veterinarians [13,44]. Adding recordings to a patient's electronic medical record also helps to document future disease progression [13].…”

Section: Discussionmentioning

confidence: 99%

Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students

Wood,

Shapter,

Stewart

2024

Animals

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Auscultation of heart sounds is an important veterinary skill requiring an understanding of anatomy, physiology, pathophysiology and pattern recognition. This cross-sectional study was developed to evaluate a targeted, audio-visual training resource for veterinary students to improve their understanding and auscultation of common heart conditions in horses. Fourth- and fifth-year 2021 and 2022 Bachelor of Veterinary Science students at the University of Queensland (UQ) were provided the learning resource and surveyed via online pre- and post-intervention surveys. Results were quantitatively analyzed using descriptive statistics and Mann–Whitney U tests. Open-ended survey questions were qualitatively analyzed by thematic analysis and Leximancer™ Version 4 program software analysis. Over the two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the pre-intervention survey and 57 completed the post-intervention survey. Quantitative results showed the resource helped students prepare for practicals and their perception of competency and confidence when auscultating equine cardiac sounds improved (p < 0.05). Compared to fifth-year students, fourth-year students felt less competent at identifying murmurs and arrythmias prior to accessing the learning resource (p < 0.05). Fourth-year and fifth-year students’ familiarity with detection of murmurs improved after completing the learning resource (p < 0.001). Qualitative analysis demonstrated a limited number of opportunities to practice equine cardiac auscultation throughout the veterinary degree, especially during the COVID-19 pandemic, and that integrated audio-visual resources are an effective means of teaching auscultation.

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Section: Discussionmentioning

confidence: 99%

Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students

Wood,

Shapter,

Stewart

2024

Animals

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“…Recently, a small, portable device for auscultation called the Stemoscope (Hulu Devices, California, USA) was developed. Fan et al [32] evaluated this and concluded that in-person auscultation and tele-auscultation by the Stemoscope were in agreement with manual acoustic auscultation and that the Stemoscope can be used in telemedicine.…”

Section: Auscultationmentioning

confidence: 91%

Remote, automatic, digital preanesthetic evaluation – are we there yet?

Pasternak,

Szczeklik,

Białka

et al. 2024

ait

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In recent years there have been multiple advancements in information technology, specifically e-health and telemedicine. Many of these advancements stemmed from the need to ensure patient and doctor safety during the coronavirus disease 2019 (COVID-19) pandemic. Patient evaluation without face-to-face contact to minimise the risk of viral transmission has become a new safety standard for doctors in many specialties.In this review, we examine the current state of the preanaesthetic evaluation and analyse its components and their potential for remote execution using current and potential future telemedical facilities and technologies. We review the literature regarding the beneficiaries of remote preanaesthetic evaluation. Finally, we discuss which aspects of the preanaesthetic assessment can be performed automatically. RATIONALE FOR REMOTE PREANESTHETIC EVALUATION Social distancing during the COVID-19 pandemicZhang et al. [1] reviewed the effectiveness of virtual perioperative assessment for surgical patients

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“…Remote auscultation, particularly through electronic stethoscope systems, plays a critical role in telemedical physical examinations. 8 – 14 Our previous study found that conventional auscultation was superior to that of real-time remote cardiac auscultation. 13 Thus, an appropriate support system is required to improve remote cardiac auscultation.…”

Section: Introductionmentioning

confidence: 99%

Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial

Hirosawa,

Sakamoto,

Harada

et al. 2024

DIGITAL HEALTH

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Background The utility of a clinical decision support system using a machine learning (ML) model for simultaneous cardiac and pulmonary auscultation is unknown. Objective This study aimed to develop and evaluate an ML system's utility for cardiopulmonary auscultation. Methods First, we developed an ML system for cardiopulmonary auscultation, using cardiopulmonary sound files from our previous study. The technique involved pre-processing, feature extraction, and classification through several neural network layers. After integration, the output class was categorized as “normal,” “abnormal,” or “undetermined.” Second, we evaluated the ML system with 24 junior residents in an open-label randomized controlled trial at a university hospital. Participants were randomly assigned to the ML system group (intervention) or conventional auscultation group (control). During training, participants listened to four cardiac and four pulmonary sounds, all of which were correctly classified. Then, participants classified a series of 16 simultaneous cardiopulmonary sounds. The control group auscultated the sounds using noise-cancelling headphones, while the intervention group did so by watching recommendations from the ML system. Results The total scores for correctly identified normal or abnormal cardiopulmonary sounds in the intervention group were significantly higher than those in the control group (366/384 [95.3%] vs. 343/384 [89.3%], P = 0.003). The cardiac test score in the intervention group was better (111/192 [57.8%] vs. 90/192 [46.9%], P = 0.04); there was no significant difference in pulmonary auscultation. Conclusions The ML-based system improved the accuracy of cardiopulmonary auscultation for junior residents. This result suggests that the system can assist early-career physicians in accurate screening.

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Real-world evaluation of the Stemoscope electronic tele-auscultation system

Cited by 10 publications

References 36 publications

Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students

Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students

Remote, automatic, digital preanesthetic evaluation – are we there yet?

Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial

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