비대면 원격진단을 위한 디지털 검이경 청진기 헬스케어 플랫폼 개발
Development of a Digital Otoscope-Stethoscope Healthcare Platform for Telemedicine
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
: We developed a device that integrates digital otoscope and stethoscope for telemedicine. The integrated device was utilized for the collection of tympanic membrane images and cardiac auscultation data. Data accumulated on the platform serv- er can support real-time diagnosis of heart and eardrum diseases using artificial intelligence. Public data from Kaggle were used for deep learning. After comparing with various deep learning models, the MobileNetV2 model showed superior performance in analyzing tympanic membrane data, and the VGG16 model excelled in analyzing cardiac data. The classification algorithm achieved an accuracy of 89.9% for eardrums data and 100% for heart sound data. These results demonstrate the possibility of diagnosing diseases without the limitations of time and space by using this platform.
Abstract
: We developed a device that integrates digital otoscope and stethoscope for telemedicine. The integrated device was utilized for the collection of tympanic membrane images and cardiac auscultation data. Data accumulated on the platform serv- er can support real-time diagnosis of heart and eardrum diseases using artificial intelligence. Public data from Kaggle were used for deep learning. After comparing with various deep learning models, the MobileNetV2 model showed superior performance in analyzing tympanic membrane data, and the VGG16 model excelled in analyzing cardiac data. The classification algorithm achieved an accuracy of 89.9% for eardrums data and 100% for heart sound data. These results demonstrate the possibility of diagnosing diseases without the limitations of time and space by using this platform.
참고문헌 (30)
- Jung KH, "Short-term exposure to PM(2.5)and vanadium and changes in asthma gene DNA methylation and lung function decrements among urban children", Respir Res, 18(1), pp.63~ (2017)
- Labarca G, "Association between inflammatory bowel disease and chronic obstructive pulmonary disease : a systematic review and meta-analysis", BMC Pulm Med, 19(1), pp.186~ (2019)
- Schraufnagel DE, "Air Pollution and Noncommunicable Diseases : A Review by the Forum of International Respiratory Societies' Environmental Committee, Part 1 : The Damaging Effects of Air Pollution", Chest, 155(2), pp.409~416 (2019)
- Ma Y, "Telemedicine application in patients with chronic disease : a systematic review and meta-analysis", BMC Med Inform Decis Mak, 22(1), pp.105~ (2022)
- Wang H, "Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic", Prim Health Care Res Dev, 22, pp.e48~ (2021)
- D'Onofrio KL, "Tele-Audiology : Current State and Future Directions", Front Digit Health, 3, pp.788103~ (2021)
- Dendere R, "A review of cellphone microscopy for disease detection", J Microsc, 260(3), pp.248~259 (2015)
- Mousseau S, "Diagnosing acute otitis media using a smartphone otoscope; a randomized controlled trial", Am J Emerg Med, 36(10), pp.1796~1801 (2018)
- Hafke-Dys H, "Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients", Front Physiol, 12, pp.745635~ (2021)
- Kim Y, "The coming era of a new auscultation system for analyzing respiratory sounds", BMC Pulm Med, 22(1), pp.119~ (2022)
- Kim Y, "Evolution of the Stethoscope : Advances with the Adoption of Machine Learning and Development of Wearable Devices", Tuberc Respir Dis(Seoul), 86(4), pp.251~263 (2023)
- McDaniel NL, "Comparison of a Novel Handheld Telehealth Device with Stand-Alone Examination Tools in a Clinic Setting", Telemed J E Health, 25(12), pp.1225~1230 (2019)
- Cha D, "Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database", EBioMedicine, 45, pp.606~614 (2019)
- Chen J, "Signal Quality Assessment of PPG Signals using STFT Time-Frequency Spectra and Deep Learning Approaches", Annu Int Conf IEEE Eng Med Biol Soc, 2021, pp.1153~1156 (2021)
- Goto R, "Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis", IEEE Access, 12, pp.32349~32360 (2024)
- Khan MA, "Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks", Neural Netw, 126, pp.384~394 (2020)
- Park YS, "Deep Learning Techniques for Ear Diseases Based on Segmentation of the Normal Tympanic Membrane", Clin Exp Otorhinolaryngol, 16(1), pp.28~36 (2023)
- Yadav K, "Deep learning based cardiovascular disease diagnosis system from heartbeat sound", International Journal of Speech Technology (2021)
- Rao D, "OTONet : Deep Neural Network for Precise Otoscopy Image Classification", IEEE Access, 12, pp.7734~7746 (2024)
- Chowdhury MEH, "Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring", Sensors (Basel), 19(12) (2019)
- Quiceno AF, "Effective phonocardiogram segmentation using nonlinear dynamic analysis and high-frequency decomposition", 2008 Computers in Cardiology (2008)
- Lee K, "Development and Implementation of Noise-Canceling Technology for Digital Stethoscope", Journal of Biomedical Engineering Research, 34(4), pp.204~211 (2013)
- eardrum dataset, "Tympanic membrane", pp.2022
- King E, "Heartbeat Sounds" (2016)
- Akay M, "Deep Learning Classification of Systemic Sclerosis Skin Using the MobileNetV2 Model", IEEE Open J Eng Med Biol, 2, pp.104~110 (2021)
- Dong N, "Inception v3 based cervical cell classification combined with artificially extracted features", Applied Soft Computing, pp.93~ (2020)
- Guefrechi S, "Deep learning based detection of COVID-19 from chest X-ray images", Multimed Tools Appl, 80, pp.31803~31820 (2021)
- Younis A, "Brain Tumor Analysis Using Deep Learning and VGG16 Ensembling Learning Approaches", Applied Sciences, 12(14) (2022)
- Ma N, "ShuffleNet V2 : Practical Guidelines for Efficient CNN Architecture Design", Computer Vision – ECCV 2018. Lecture Notes in Computer Science2018, pp.122~138 (2018)
- Ji Q, "Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images", Algorithms, 12(3) (2019)
