A medical percussion instrument using a wavelet-based method for archivable output and automatic classification

Ayodele, Kayode P.; Ogunlade, Oluwadare; Olugbon, O. J.; Akinwale, Olawale Babatunde; Kehinde, Lawrence O.

doi:10.1016/j.compbiomed.2020.104100

Cited by 8 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While this proof of concept was designed to determine the best technical approach for a robotic percussion, a major next step will be the evaluation of the ventral motorized instrument regarding detection of different pathologies. Furthermore, different areas of medicine already use automatized analysis, such as deep learning approaches, for medical audio signals [2,11,17,22].…”

Section: Discussionmentioning

confidence: 99%

“…Previous publications introducing mechanical devices [2,3,19,21] only tested their constructions for a ventral percussion, whereby patients had to hold devices themselves. Even though Guarino [10] presented the technique of ventral sternal chest percussion with a simultaneous dorsal auscultation in his original publication as efficient, later studies questioned this technique [4,5,16].…”

Section: Telemedical Diagnostic Systemmentioning

confidence: 99%

“…However, current available equipment is limited to a small set of parameters and full reproduction of an in person examination in telemedicine is not available [7]. Regarding an automatic percussion, only few devices have been proposed yet [2,3,19,21]. While Peng and Dai [19] used a permanent magnet shaker, Ayodele and Ogunlade [2] applied a push-pull solenoid plessor for the percussion generation.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding an automatic percussion, only few devices have been proposed yet [2,3,19,21]. While Peng and Dai [19] used a permanent magnet shaker, Ayodele and Ogunlade [2] applied a push-pull solenoid plessor for the percussion generation. Rao and Ruiz [21] utilized a neodymium audio driver and Bohadana and Kraman [3] assessed a pneumatic cylinder.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Telemedical percussion: objectifying a fundamental clinical examination technique for telemedicine

et al. 2021

View full text Add to dashboard Cite

Purpose While demand for telemedicine is increasing, patients are currently restricted to tele-consultation for the most part. Fundamental diagnostics like the percussion still require the in person expertize of a physician. To meet today’s challenges, a transformation of the manual percussion into a standardized, digital version, ready for telemedical execution is required. Methods In conjunction with a comprehensive telemedical diagnostic system, in which patients can get examined by a remote-physician, a series of three robotic end-effectors for mechanical percussion were developed. Comprising a motor, a magnetic and a pneumatic-based version, the devices strike a pleximeter to perform the percussion. Emitted sounds were captured using a microphone-equipped stethoscope. The 84 recordings were further integrated into a survey in order to classify lung and non-lung samples. Results The study with 21 participants comprised physicians, medical students and non-medical-related raters in equal parts. With 71.4% correctly classified samples, the ventral motorized device prevailed. While the result is significantly better compared to a manual or pneumatic percussion in this very setup, it only has a small edge over the magnetic devices. In addition, for all ventral versions non-lung regions were rather correctly identified than lung regions. Conclusion The overall setup proves the feasibility of a telemedical percussion. Despite the fact, that produced sounds differ compared to today’s manual technique, the study shows that a standardized mechanical percussion has the potential to improve the gold standard’s accuracy. While further extensive medical evaluation is yet to come, the system paves the way for future uncompromised remote examinations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Telemedical Diagnostic Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Telemedical percussion: objectifying a fundamental clinical examination technique for telemedicine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Percussion is performed by administering a mechanical impact, using a percussion hammer or fingers. Medical personnel determine the position, size, consistency, and boundaries of fundamental organs and their associated pathologies, by interpreting the sound's amplitude and pitch (Ayodele et al, 2020 ). The difference between normal and abnormal tissues appears to be a phenomenon such as “clear and long-lasting sound described as resonance,” owing to impedance discrepancies in the inspected area (Yernault and Bohadana, 1995 ).…”

Section: Introductionmentioning

confidence: 99%

iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform

et al. 2022

View full text Add to dashboard Cite

Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.

show abstract

Automated identification of steel weld defects, a convolutional neural network improved machine learning approach

Shu,

Wu,

et al. 2024

Front. Struct. Civ. Eng.

View full text Add to dashboard Cite

A medical percussion instrument using a wavelet-based method for archivable output and automatic classification

Cited by 8 publications

References 36 publications

Telemedical percussion: objectifying a fundamental clinical examination technique for telemedicine

Telemedical percussion: objectifying a fundamental clinical examination technique for telemedicine

iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform

Automated identification of steel weld defects, a convolutional neural network improved machine learning approach

Contact Info

Product

Resources

About