scholarly journals Influence of observer preferences and auscultatory skill on the choice of terms to describe lung sounds: a survey of staff physicians, residents and medical students

2020 ◽  
Vol 7 (1) ◽  
pp. e000564
Author(s):  
Abraham Bohadana ◽  
Hava Azulai ◽  
Amir Jarjoui ◽  
George Kalak ◽  
Gabriel Izbicki
Keyword(s):  
Medical Students ◽  
Breath Sound ◽  
Computer Assisted ◽  
Learning Tools ◽  
Lung Sounds ◽  
Lung Sound ◽  
Audio Recordings ◽  
Self Learning ◽  
Wide Dissemination ◽  
Group Agreement

BackgroundIn contrast with the technical progress of the stethoscope, lung sound terminology has remained confused, weakening the usefulness of auscultation. We examined how observer preferences regarding terminology and auscultatory skill influenced the choice of terms used to describe lung sounds.MethodsThirty-one staff physicians (SP), 65 residents (R) and 47 medical students (MS) spontaneously described the audio recordings of 5 lung sounds classified acoustically as: (1) normal breath sound; (2) wheezes; (3) crackles; (4) stridor and (5) pleural friction rub. A rating was considered correct if a correct term or synonym was used to describe it (term use ascribed to preference). The use of any incorrect terms was ascribed to deficient auscultatory skill.ResultsRates of correct sound identification were: (i) normal breath sound: SP=21.4%; R=11.6%; MS=17.1%; (ii) wheezes: SP=82.8%; R=85.2%; MS=86.4%; (iii) crackles: SP=63%; R=68.5%; MS=70.7%; (iv) stridor: SP=92.8%; R=90%; MS=72.1% and (v) pleural friction rub: SP=35.7%; R=6.2%; MS=3.2%. The 3 groups used 66 descriptive terms: 17 were ascribed to preferences regarding terminology, and 49 to deficient auscultatory skill. Three-group agreement on use of a term occurred on 107 occasions: 70 involved correct terms (65.4%) and 37 (34.6%) incorrect ones. Rate of use of recommended terms, rather than accepted synonyms, was 100% for the wheezes and the stridor, 55% for the normal breath sound, 22% for the crackles and 14% for the pleural friction rub.ConclusionsThe observers’ ability to describe lung sounds was high for the wheezes and the stridor, fair for the crackles and poor for the normal breath sound and the pleural friction rub. Lack of auscultatory skill largely surpassed observer preference as a factor determining the choice of terminology. Wide dissemination of educational programs on lung auscultation (eg, self-learning via computer-assisted learning tools) is urgently needed to promote use of standardised lung sound terminology.

ANALYSIS OF STUDENT ACTIVITIES ON COMPUTER - A STUDY ON PUNJAB UNIVERSITY, INDIA

2012 ◽  
Vol 3 (3) ◽  
pp. 354-358
Author(s):  
Dr Gunmala Suri ◽  
Sneha Sharma
Keyword(s):  
Learning Activities ◽  
Information Collection ◽  
Learning Tools ◽  
Student Activities ◽  
Group Activities ◽  
E Learning ◽  
Faculty Department ◽  
Self Learning

The purpose of this research is to investigate and understand how students are using computer. The activities that a student undertakes with the help of computers which might be fulfilling some academic or non academic purpose, is of great interest. It will help in understanding the limitations and potentials offered by the technology for use of computer in classroom. This paper brings out the three major kinds of activities that students undertake with computer; self learning activities, Information collection tasks and communication and group activities. The study further analyses the effect of demographics i.e. gender, age and faculty (department) of students on the activities with computer. The results show that gender has no impact on the activities of students with computer. The age impacts only the activities related to Information collection by using computer where as the faculty of student significantly impacts all the activities viz. self learning activities, Information collection tasks and communication and group activities. The findings from this research can be used in designing future e-learning initiatives and development e-learning tools

scholarly journals Influence of language skills on the choice of terms used to describe lung sounds in a language other than English: a cross-sectional survey of staff physicians, residents and medical students

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e044240
Author(s):  
Abraham Bohadana ◽  
Hava Azulai ◽  
Amir Jarjoui ◽  
George Kalak ◽  
Ariel Rokach ◽  
...  
Keyword(s):  
Medical Students ◽  
Dual Language ◽  
Language Skills ◽  
Cross Sectional Study ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
C Language ◽  
Audio Recordings ◽  
Chest Auscultation ◽  
English Speaking

IntroductionThe value of chest auscultation would be enhanced by the use of a standardised terminology. To that end, the recommended English terminology must be transferred to a language other than English (LOTE) without distortion.ObjectiveTo examine the transfer to Hebrew—taken as a model of LOTE—of the recommended terminology in English.Design/settingCross-sectional study; university-based hospital.Participants143 caregivers, including 31 staff physicians, 65 residents and 47 medical students.MethodsObservers provided uninstructed descriptions in Hebrew and English of audio recordings of five common sounds, namely, normal breath sound (NBS), wheezes, crackles, stridor and pleural friction rub (PFR).Outcomes(a) Rates of correct/incorrect classification; (b) correspondence between Hebrew and recommended English terms; c) language and auscultation skills, assessed by crossing the responses in the two languages with each other and with the classification of the audio recordings validated by computer analysis.ResultsRange (%) of correct rating was as follows: NBS=11.3–20, wheezes=79.7–87.2, crackles=58.6–69.8, stridor=67.4–96.3 and PFR=2.7–28.6. Of 60 Hebrew terms, 11 were correct, and 5 matched the recommended English terms. Many Hebrew terms were adaptations or transliterations of inadequate English terms. Of 687 evaluations, good dual-language and single-language skills were found in 586 (85.3%) and 41 (6%), respectively. However, in 325 (47.3%) evaluations, good language skills were associated with poor auscultation skills.ConclusionPoor auscultation skills surpassed poor language skills as a factor hampering the transfer to Hebrew (LOTE) of the recommended English terminology. Improved education in auscultation emerged as the main factor to promote the use of standardised lung sound terminology. Using our data, a strategy was devised to encourage the use of standardised terminology in non-native English-speaking countries.

scholarly journals Crackle Pitch Rises Progressively during Inspiration in Pneumonia, CHF, and IPF Patients

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Andrey Vyshedskiy ◽  
Raymond Murphy
Keyword(s):  
Heart Failure ◽  
Lung Volume ◽  
Interstitial Fibrosis ◽  
Lung Sounds ◽  
Lung Sound ◽  
Lung Volumes ◽  
Alternate Explanation ◽  
40 Hz

Objective. It is generally accepted that crackles are due to sudden opening of airways and that larger airways produce crackles of lower pitch than smaller airways do. As larger airways are likely to open earlier in inspiration than smaller airways and the reverse is likely to be true in expiration, we studied crackle pitch as a function of crackle timing in inspiration and expiration. Our goal was to see if the measurement of crackle pitch was consistent with this theory.Methods. Patients with a significant number of crackles were examined using a multichannel lung sound analyzer. These patients included 34 with pneumonia, 38 with heart failure, and 28 with interstitial fibrosis.Results. Crackle pitch progressively increased during inspirations in 79% of all patients. In these patients crackle pitch increased by approximately 40 Hz from the early to midinspiration and by another 40 Hz from mid to late-inspiration. In 10% of patients, crackle pitch did not change and in 11% of patients crackle pitch decreased. During expiration crackle pitch progressively decreased in 72% of patients and did not change in 28% of patients.Conclusion. In the majority of patients, we observed progressive crackle pitch increase during inspiration and decrease during expiration. Increased crackle pitch at larger lung volumes is likely a result of recruitment of smaller diameter airways. An alternate explanation is that crackle pitch may be influenced by airway tension that increases at greater lung volume. In any case improved understanding of the mechanism of production of these common lung sounds may help improve our understanding of pathophysiology of these disorders.

scholarly journals Effects of competitive learning tools on medical students: A case study

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194096 ◽  
Author(s):  
Alfredo Corell ◽  
Luisa M. Regueras ◽  
Elena Verdú ◽  
María J. Verdú ◽  
Juan P. de Castro
Keyword(s):  
Medical Students ◽  
Competitive Learning ◽  
Learning Tools

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

2021 ◽  
Author(s):  
Sibghatullah I. Khan ◽  
Vikram Palodiya ◽  
Lavanya Poluboyina
Keyword(s):  
Phase Space ◽  
Human Lung ◽  
Feature Space ◽  
Normal Lung ◽  
Space Representation ◽  
Support Vector ◽  
Lung Sounds ◽  
Lung Sound ◽  
Phase Space Representation

Abstract Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

Computer-assisted instruction in cancer for third-year medical students using the physician data query (PDQ) system

1989 ◽  
Vol 4 (1) ◽  
pp. 11-15 ◽  
Author(s):  
Q. Scott Ringenberg ◽  
E. Diane Johnson ◽  
Donald Doll ◽  
Sharon Anderson ◽  
John Yarbro
Keyword(s):  
Medical Students ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Data Query ◽  
Assisted Instruction

scholarly journals Computer-Assisted Teaching of Skin Flap Surgery: Validation of a Mobile Platform Software for Medical Students

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e65833 ◽  
Author(s):  
David P. de Sena ◽  
Daniela D. Fabricio ◽  
Maria Helena I. Lopes ◽  
Vinicius D. da Silva
Keyword(s):  
Medical Students ◽  
Skin Flap ◽  
Mobile Platform ◽  
Computer Assisted ◽  
Flap Surgery

scholarly journals Computer-assisted teaching of nutritional anemias and diabetes to first-year medical students

2002 ◽  
Vol 75 (1) ◽  
pp. 154-161 ◽  
Author(s):  
Maciej S Buchowski ◽  
Claudia Plaisted ◽  
Jane Fort ◽  
Steven H Zeisel
Keyword(s):  
Medical Students ◽  
Computer Assisted ◽  
First Year ◽  
First Year Medical Students

scholarly journals Classification of Lung Sounds and Disease Prediction using Dense CNN Network

2021 ◽  
Vol 11 (1) ◽  
pp. 195-198
Author(s):  
Suyash Lakhani ◽  
◽  
Ridhi Jhamb ◽  
Keyword(s):  
Respiratory Sounds ◽  
Respiratory Sound ◽  
Lung Sounds ◽  
Dense Network ◽  
Lung Sound ◽  
Respiratory Dysfunction ◽  
Respiratory Disorders ◽  
Respiratory Illnesses ◽  
In The Beginning

Respiratory illnesses are a main source of death in the world and exact lung sound identification is very significant for the conclusion and assessment of sickness. Be that as it may, this method is vulnerable to doctors and instrument limitations. As a result, the automated investigation and analysis of respiratory sounds has been a field of great research and exploration during the last decades. The classification of respiratory sounds has the potential to distinguish anomalies and diseases in the beginning phases of a respiratory dysfunction and hence improve the accuracy of decision making. In this paper, we explore the publically available respiratory sound database and deploy three different convolutional neural networks (CNN) and combine them to form a dense network to diagnose the respiratory disorders. The results demonstrate that this dense network classifies the sounds accurately and diagnoses the corresponding respiratory disorders associated with them.

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