BACKGROUND: The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential.
OBJECTIVE: This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats.
METHODS: A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence).
RESULTS: The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation.
CONCLUSIONS: To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

UNASSIGNED: Healthcare-associated infections cause high mortality and morbidity, and lack of stethoscope disinfection is one of the reasons for healthcare-associated infections. Nurses who frequently use stethoscopes in the clinic do not disinfect stethoscopes at high rates. This study aimed to identify the frequency of stethoscope disinfection by nurses and their knowledge about the same.
UNASSIGNED: This was a mixed-methods observational study. The quantitative part of the study included 202 nurses, the qualitative part included 12. Two researchers who made observations during stethoscope use recorded the procedures the nurses performed on the \"Observation Form\". Semi-structured in-depth interviews were conducted based on phenomenological methods.
UNASSIGNED: 23.7% of the nurses disinfected their stethoscopes before contact with patients, 11.8% after contact with patients and 6.4% before and after contact with patients. The nurses used a stethoscope on an average of 7.42 patients without disinfecting it. In the qualitative interview, some nurses stated that they did not have information about the disinfectants to be used for stethoscopes and their effectiveness. Some of the participants in the present study stated that they did not receive training on stethoscope disinfection and that they did not know that there were guidelines about it.
UNASSIGNED: Since there were deficiencies in the implementation of stethoscope disinfection as well as knowledge, the transfer of knowledge in this context must receive more attention in education and training.
UNASSIGNED: Healthcare-assoziierte Infektionen verursachen hohe Mortalität und Morbidität; einer der Gründe ist die unterlassene Desinfektion von Stethoskopen. Da Krankenschwestern und -pfleger, die in der Klinik häufig Stethoskope verwenden, häufig die Stethoskope nicht desinfizieren, sollten die Häufigkeit der Desinfektion von Stethoskopen und das Wissen darüber ermittelt werden.
UNASSIGNED: Es handelte sich um eine Beobachtungsstudie. In den quantitativen Teil der Studie wurden 202 Krankenschwestern einbezogen, in den qualitativen Teil 12 Krankenschwestern. Zwei Untersucher, die die Stethoskopdesinfektion beobachteten, hielten die von den Krankenschwestern durchgeführten Verfahren in einem Beobachtungsformblatt fest. Im qualitativen Teil wurden halbstrukturierte Tiefeninterviews auf der Grundlage phänomenologischer Methoden durchgeführt.
UNASSIGNED: Es wurde festgestellt, dass 23,7% der Pflegenden ihre Stethoskope vor dem Kontakt mit dem Patienten, 11,8% nach dem Kontakt mit dem Patienten und 6,4% (n=13) vor und nach dem Kontakt mit dem Patienten desinfizierten. Durchschnittlich benutzten die Krankenschwestern ein Stethoskop bei 7,42 Patienten ohne zwischenzeitliche Desinfektion. Aus den Interviewergebnissen geht hervor, dass einige Pflegende angaben, sie hätten keine Informationen über die bei der Stethoskopreinigung zu verwendenden Desinfektionsmittel und deren Wirksamkeit. Einige gaben an, dass sie keine Schulung zur Stethoskopdesinfektion erhalten hatten und nicht wussten, dass es diesbezüglich Richtlinien gibt.
UNASSIGNED: Da es sowohl Mängel in der Durchführung der Stethoskopdesinfektion als auch diesbezügliche Wissensdefizite gab, ist die Wissensvermittlung hierzu in der Aus- und Weiterbildung einschließlich der Schulung entsprechend zu berücksichtigen.

BACKGROUND: The stethoscope is the most widely used instrument in healthcare. Studies have found similar rates of contamination on the stethoscope diaphragm and on physician fingertips after a single examination. Our aim was to test the effectiveness of an innovative portable device for disinfecting stethoscope membranes.
METHODS: From November 2016 to May 2017, a cross-sectional study was conducted in four wards of a private clinic: General Ward (GW), Internal Medicine Ward (IMW), Post-Operative Observation Ward (POW) and Permanent Vegetative State Ward (PVSW). Five wearable medical devices, designed to disinfect stethoscope membranes automatically by means of UV-C radiation, were provided to operators. Spot checks were made for microbial counts of stethoscope membranes, classified as treated or otherwise on the basis of whether they were found coupled or otherwise with the devices. The percentage reduction in colony forming units (CFU) was calculated between the two groups.
RESULTS: The number of tests of stethoscopes treated with the device was 116 out of 272. Untreated samples had a mean contamination of 132.2 CFU versus 6.9 CFU of treated samples: a 94.8% reduction (95% CI 91.3%-97.7). Highly significant statistical differences in CFU were found between untreated and treated membranes (p < 0.001). In particular, microbial contamination showed a reduction of 88.7% (CI 77.5%-96.05%) in PVSW, 95.9% (CI 88.2%-98.5%) in GW, 84.5% (CI 76.4%-90.5%) in IMW and 95.8% (CI 90.3%-98.1%) in POW.
CONCLUSIONS: The devices proved effective and efficient in reducing the microbial load of stethoscope membranes. Wearing the device on the coat may act as a reminder of the need for hygiene.

Auscultation is a fundamental diagnostic technique that provides valuable diagnostic information about different parts of the body. With the increasing prevalence of digital stethoscopes and telehealth applications, there is a growing trend towards digitizing the capture of bodily sounds, thereby enabling subsequent analysis using machine learning algorithms. This study introduces the SonicGuard sensor, which is a multichannel acoustic sensor designed for long-term recordings of bodily sounds. We conducted a series of qualification tests, with a specific focus on bowel sounds ranging from controlled experimental environments to phantom measurements and real patient recordings. These tests demonstrate the effectiveness of the proposed sensor setup. The results show that the SonicGuard sensor is comparable to commercially available digital stethoscopes, which are considered the gold standard in the field. This development opens up possibilities for collecting and analyzing bodily sound datasets using machine learning techniques in the future.

Born in a wealthy family with a tradition in science, Henry Ingersoll Bowditch (1808-1882) with studies at Harvard Medical School and in Europe had succeeded in leaving his mark in the American history of medicine. He had been a pioneer in the stethoscope\'s use, which was promoted and suggested to all physicians of his era. He had widely used thoracentesis, an ancient procedure, for pleuritic effusions, diagnosed with a stethoscope. Inside his most popular treatise \"The Young Stethoscopist,\" he had given a plethora of data concerning the auscultation of the lungs, heart, and vessels; obstetrics; and veterinary. To help younger physicians, he demonstrated through figures local anatomy and positions for auscultation, providing information for various types of stethoscopes being in use during the mid-19th century. He was a humanist and reformer for public hygiene. This historical vignette aims to present Henry Ingersoll Bowditch and his work concerning the thorax. For his contributions to education and public hygiene, he should be celebrated as one of the most important figures of the eve of American medicine.

OBJECTIVE: The management of complications of arteriovenous fistula (AVF) for hemodialysis, principally stenosis, remains a major challenge for clinicians with a substantial impact on health resources. Stenosis not infrequently preludes to thrombotic events with the loss of AVF functionality. A functioning AVF, when listened by a stethoscope, has a continuous systolic-diastolic low-frequency murmur, while with stenosis, the frequency of the murmur increases and the duration of diastolic component decreases, disappearing in severe stenosis. These evidences are strictly subjective and dependent from operator skill and experience. New generation digital stethoscopes are able to record sound and subsequently dedicated software allows to extract quantitative variables that characterize the sound in an absolutely objective and repeatable way. The aim of our study was to analyze with an appropriate software sounds from AVFs taken by a commercial digital stethoscope and to investigate the potentiality to develop an objective way to detect stenosis.
METHODS: Between September 2022 and January 2023, 64 chronic hemodialysis (HD) patients were screened by two blinded experienced examiners for recognized criteria for stenosis by Doppler ultrasound (DUS) and, consequently, the sound coming from the AVFs using a 3 M™ Littmann® CORE Digital Stethoscope 8570 in standardized sites was recorded. The sound waves were transformed into quantitative variables (amplitude and frequency) using a sound analysis software. The practical usefulness of the core digital stethoscope for a quick identification of an AVF stenosis was further evaluated through a pragmatic trial. Eight young nephrologist trainees underwent a simple auscultatory training consisting of two sessions of sound auscultation focusing two times on a \"normal\" AVF sound by placing the digital stethoscope on a convenience site of a functional AVF.
RESULTS: In 48 patients eligible, all sound components displayed, alone, a remarkable diagnostic capacity. More in detail, the AUC of the average power was 0.872 [95% CI 0.729-0.951], while that of the mean normalized frequency was 0.822 [95% 0.656-0.930]. From a total of 32 auscultations (eight different block sequences, each one comprising four auscultations), the young clinicians were able to identify the correct sound (stenosis/normal AVF) in 25 cases, corresponding to an overall accuracy of 78.12% (95% CI 60.03-90.72%).
CONCLUSIONS: The analysis of sound waves by a digital stethoscope permitted us to distinguish between stenotic and no stenotic AVFs. The standardization of this technique and the introducing of data in a deep learning algorithm could allow an objective and fast method for a frequent monitoring of AVF.

Emergency and disaster medical care often face resource or equipment shortages. 3D printing technology has been proven to be effective in cases with insufficient supply chains. MAYO tubes and stethoscopes are essential components of ABCDE patient examinations; however, 3D-printed variants have not been fully tested. These 3D-printed instruments were substituted and validated in a simulated pre-hospital environment. In total, 26 participants were included in this study. Fifteen clinicians or paramedics with at least 3 years of professional experience and 10 medical students. One student was excluded because he had relevant experience with emergency care. As basic tasks, the placement of MAYO tubes and auscultation with stethoscopes were performed using medical simulators. 3D printed instruments were compared with conventional clinical devices by measuring the time required for the intervention, success rate, and user satisfaction. In the study FFF (Fused Filament Fabrication (FFF), SLS (Selective Laser Sintering (SLS), and SLA (stereolithography) 3D printing were used in this study. The times required for implementation and auscultation were examined for each instrument. There was no significant difference between the MAYO tube (p = 0.798) and the stethoscope (p = 0.676). In the case of stethoscopy, the study investigated the correct diagnosis, and no significant difference was found (p = 0.239), although an interesting trend was observed. Regarding the MAYO tube, the study found no significant difference in correct position formation (p = 0.163). The experience levels of the groups did not influence these factors. However, significant differences in user satisfaction were found in both cases in favour of the conventional versions (p < 0.001). Overall, the results of this study suggest that 3D-printed devices could be suitable replacements for clinic-based devices in emergency situations. The 3D-printed devices did not perform inferiorly at any of the indicated points compared to their classical counterparts. However, the practical applicability of the devices used in this study requires further investigation.

UNASSIGNED: The hospital environment serves as a niche for pathogenic microorganisms, so efforts are constantly being made to identify the potential mode of microbial pathogen transmission causing clinical infections.
UNASSIGNED: The aim of this study was to microbiologically examine the stethoscopes used by clinicians at the University of Benin Teaching Hospital (UBTH) in Benin, Nigeria.
UNASSIGNED: A total of 106 clinicians\' stethoscopes were cleaned using cotton-tipped swabs dampened with normal saline. This included both earpieces along with the diaphragm (three samples per stethoscope). The samples were then sent to the Medical Microbiology Laboratory of UBTH and processed immediately as per the standard guidelines. The emergent colonies were subsequently identified, and antimicrobial susceptibility tests were performed.
UNASSIGNED: A total of 114 (35.8%) bacterial isolates were recovered, including Staphylococcus aureus (S. aureus) (33.3%), coagulase-negative staphylococci (CoNS) (33.3%), Bacillus spp. (22.8%), Acinetobacter spp. (5.3%), Escherichia coli (E. coli) (1.8%) and Klebsiella spp. (3.5%). Diaphragms had the highest yield of methicillin-resistant S. aureus (MRSA) (46.2%) and CoNS (17.9%). Age (P = 0.0387) and cadre of clinician (P = 0.0043) were risk factors for contamination, whereas clinicians who never cleaned their stethoscopes (P = 0.0044) or cleaned only the earpieces (P = 0.0001) had more contaminated stethoscopes.
UNASSIGNED: The contamination rate of stethoscopes used by clinicians in Benin City was 56.6%. There is a need to establish proper stethoscope cleaning practices for all cadres of personnel in clinical practice to minimise health risks to patients and healthcare workers (HCW).

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

UNASSIGNED: Stethoscopes carry a significant risk for pathogen transmission. Here, the safe use and performance of a new, non-sterile, single-use stethoscope cover (SC), that is impermeable for pathogens, was investigated by different healthcare professionals (HCPs) in the postoperative care setting of an intensive care unit (ICU).
UNASSIGNED: Fifty-four patients underwent routine auscultations with the use of the SC (Stethoglove®, Stethoglove GmbH, Hamburg, Germany). The participating HCPs (n = 34) rated each auscultation with the SC on a 5-point Likert scale. The mean ratings of acoustic quality and the SC handling were defined as primary and secondary performance endpoint.
UNASSIGNED: 534 auscultations with the SC were performed (average 15.7/user) on the lungs (36.1%), the abdomen (33.2%), the heart (28.8%), or other body-sites (1.9%). No adverse device-effects occurred. The acoustic quality was rated at 4.2 ± 0.7 (mean) with a total of 86.1% of all auscultations being rated at least as 4/5, and with no rating as below 2. The SC handling was rated at 3.7 ± 0.8 (mean) with a total of 96.4% of all auscultations being rated at least 3/5.
UNASSIGNED: Using a real-world setting, this study demonstrates that the SC can be safely and effectively used as cover for stethoscopes during auscultation. The SC may therefore represent a useful and easy-to-implement tool for preventing stethoscope-mediated infections.Study Registration: EUDAMED no. CIV-21-09-037762.