Overview of Deep Learning Methods Used in the Medical Device Industry

Year 2021, Volume: 4 Issue: 2, 68 - 74, 01.04.2021

Orhan Gündüz , Cengiz Tepe , Nurettin Şenyer , Mehmet Serhat Odabas

https://doi.org/10.34248/bsengineering.858918

Abstract

Almost everywhere in our lives, we often come across a deep learning based artificial intelligence product or application that has become the center of attraction worldwide. This is evidence of a quick development in deep learning methods and the areas where they are used. Face detection, voice recognition, self-driving, defense industry, security industry and many other areas can be shown as samples. In this study, a literature review has been made that we divided into classes according to the fields in which deep learning methods are used in the medical device industry and also where we examine the distribution of the articles by years. It is divided into six classes such as healthcare, big data and wearable technologies, biomedical signal, image processing, diagnosis and internet of medical things. As a result, the use of deep learning methods in the medical device industry has gained speed in recent years and also most studies have been done on diagnosis and image processing.

Keywords

Deep learning, Medical device, Artificial intelligence, Neural networks, Internet of medical things

Tıbbi Cihaz Sektöründe Kullanılan Derin Öğrenme Yöntemlerine Genel Bakış

Abstract

Hayatımızın hemen hemen her yerinde, dünya çapında ilgi odağı haline gelen derin öğrenme temelli bir yapay zeka ürününe veya uygulamasına sıkça rastlamaktayız. Bu durum derin öğrenme yöntemlerinde ve kullanıldığı alanlarda hızlı bir gelişme yaşandığının kanıtıdır. Bu alanlara yüz tanıma, ses tanıma, sürücüsüz araç kullanımı, savunma sanayi, güvenlik sanayi ve daha birçok alan örnek olarak gösterilebilir. Bu çalışmada, derin öğrenme yöntemlerinin tıbbi cihaz sektöründeki kullanıldığı alanlara göre sınıflara ayırdığımız ve ayrıca yapılan yayınların yıllara göre dağılımı incelediğimiz bir derleme çalışması yapılmıştır. Tıbbi cihaz sektöründe derin öğrenmenin kullanıldığı alanlar, sağlık hizmetleri, büyük veri ve giyilebilir teknolojiler, biyomedikal sinyal, görüntü işleme, teşhis ve medikal nesnelerin interneti olmak üzerine altı adet sınıfa ayrılmıştır. Sonuç olarak, derin öğrenme yöntemlerinin tıbbi cihaz sektöründe kullanımın hız kazanması son yıllarda olmuştur. En çok teşhis ve görüntü işleme alanlarında çalışmalar yapıldığı görülmüştür.

Keywords

Derin öğrenme, Tıbbi cihaz, Yapay zeka, Sinirsel ağlar, Medikal nesnelerin interneti

References

• Alhussein M, Muhammad G, Hossain MS. 2019. EEG Pathology detection based on deep learning. IEEE Access, 7, 27781–27788. https://doi.org/10.1109/access.2019.2901672.
• Ali F, El-Sappagh S, Islam SMR, Ali A, Attique M, Imran M, Kwak KS. 2021. An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generat Comput Systems, 114, 23–43. https://doi.org/10.1016/j.future.2020.07.047.
• Ali F, El-Sappagh S, Islam SMR, Kwak D, Ali A, Imran M, Kwak KS. 2020. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inform Fusion, 63: 208–222. https://doi.org/10.1016/j.inffus.2020.06.008.
• Al-Turjman F, Zahmatkesh H, Mostarda L. 2019. Quantifying uncertainty in ınternet of medical things and big-data services using ıntelligence and deep learning. IEEE Access, 7: 115749–115759.
• Amato F, Marrone S, Moscato V, Piantadosi G, Picariello A, Sansone C. 2019. HOLMeS: eHealth in the big data and deep learning era. Inform, 10(2): 34. https://doi.org/10.3390/info10020034.
• Balu A, Nallagonda S, Xu F, Krishnamurthy A, Hsu M, Sarkar S. 2019. A deep learning framework for design and analysis of surgical bioprosthetic heart valves. Sci Reports, 9: 18560, https://doi.org/10.1038/s41598-019-54707-9.
• Casal R, Di Persia LE, Schlotthauer G. 2021. Classifying sleep–wake stages through recurrent neural networks using pulse oximetry signals. Biomed Signal Proces Control, 63, 102195. https://doi.org/10.1016/j.bspc.2020.102195.
• Craik A, He Y, Contreras-Vidal JL. 2019. Deep learning for electroencephalogram (EEG classification tasks: a review. J Neural Engin, 16(3): 031001. https://doi.org/10.1088/1741-2552/ab0ab5.
• Currie G, Hawk KE, Rohren E, Vial A, Klein R. 2019. Machine learning and deep learning in medical imaging: intelligent imaging. J Med Imag Radiat Sci, 50(4): 477–487. https://doi.org/10.1016/j.jmir.2019.09.005.
• De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O’Donoghue B, Visentin D, van den Driessche G, Lakshminarayanan B, Meyer C, Mackinder F, Bouton S, Ayoub K, Chopra R, King D, Karthikesalingam A, Ronneberger O. 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Med, 24(9): 1342–1350. https://doi.org/10.1038/s41591-018-0107-6
• Deng Y. 2019. Deep learning on mobile devices: a review. Mobile Multimedia/Image Proc, Sec, Applicat, 52–66. https://doi.org/10.1117/12.2518469.
• Ding Y, Wu G, Chen D, Zhang N, Gong L, Cao M, Qin Z. 2020. DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things. IEEE Internet Things J, 8(3): 1504-1518.
• Dose H, Møller JS, Iversen HK, Puthusserypady S. 2018. An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Expert Systems with Applicat, 114: 532–542. https://doi.org/10.1016/j.eswa.2018.08.031.
• Ebigbo A, Mendel R, Probst A, Manzeneder J, Souza Jr LA, Papa JP, Palm C, Messmann H. 2018. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut, 68(7): 1143–1145. https://doi.org/10.1136/gutjnl-2018-317573
• Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui, C, Corrado, G, Thrun, S, Dean, J. 2019. A guide to deep learning in healthcare. Nature Med, 25(1): 24–29. https://doi.org/10.1038/s41591-018-0316-z.
• Fortune Business Insights. 2019. Medical devices market size, share, trends analysis report 2018-2025. URL: https://www.fortunebusinessinsights.com/industry-reports/medical-devices-market-100085 (erişim tarihi: 27 Kasım 2020).
• Fourcade A, Khonsari R H. 2019. Deep learning in medical image analysis: A third eye for doctors. J Stomatol Oral Maxillofacial Surgery, 120(4): 279–288. https://doi.org/10.1016/j.jormas.2019.06.002.
• Haghi M, Thurow K, Stoll R. 2017. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthcare Inform Res, 23(1): 4. https://doi.org/10.4258/hir.2017.23.1.4.
• Kim JY, Ro K, You S, Nam BR, Yook S, Park HS, Yoo JC, Park E, Cho K, Cho BH, Kim IY. 2019. Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning. Comput Methods Prog Biomed, 182: 105063. https://doi.org/10.1016/j.cmpb.2019.105063.
• Kim YJ, Jang H, Lee K, Park S, Min SG, Hong C, Park JH, Lee K, Kim, J, Hong W, Jung H, Liu Y, Rajkumar H, Khened M, Krishnamurthi G, Yang S, Wang X, Han CH, Kwak JT, Choi J. 2021. PAIP 2019: Liver cancer segmentation challenge. Med Image Analysis, 67: 101854.
• Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang W, Halperin JL, Narayan SM. 2019. Deep learning for cardiovascular medicine: a practical primer. European Heart J, 40(25): 2058–2073. https://doi.org/10.1093/eurheartj/ehz056.
• Kwon S, Hong J, Choi EK, Lee B, Baik C, Lee E, Jeong ER, Koo BK, Oh S, Yi Y. 2020. Detection of atrial fibrillation using a ring-type wearable device (cardiotracker and deep learning analysis of photoplethysmography signals: prospective observational proof-of-concept study. J Med Internet Res, 22(5): e16443. https://doi.org/10.2196/16443.
• Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, Choy G, Do S. 2017. Fully automated deep learning system for bone age assessment. J Digit Imag, 30(4): 427–441. https://doi.org/10.1007/s10278-017-9955-8
• Miotto R, Wang F, Wang S, Jiang X, Dudley JT. 2017. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformat, 19(6): 1236–1246. https://doi.org/10.1093/bib/bbx044.
• Ngiam KY, Khor IW. 2019. Big data and machine learning algorithms for health-care delivery. The Lancet Oncol, 20(5): e262–e273.
• Ni JC, Shpanskaya K, Han M, Lee EH, Do BH, Kuo WT, Yeom KW, Wang DS. 2020. Deep learning for automated classification of inferior vena cava filter types on radiographs. J Vascul Intervent Radiol, 31(1): 66–73. https://doi.org/10.1016/j.jvir.2019.05.026
• Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR. 2018. A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Applicat, 32(15): 10927–10933.
• Özlü CÇ. 2020. Ülkemizde tıbbi cihaz sektörü hangi yönde değişiyor? URL: https://sesanltd.com.tr/ulkemizde-tibbi-cihaz-sektoru-hangi-yonde-degisiyor (erişim tarihi: 27 Kasım 2020).
• Swayamsiddha S, Mohanty C. 2020. Application of cognitive Internet of Medical Things for COVID-19 pandemic. Diabetes Metab Syndr, 14(5): 911–915.
• Ushimaru Y, Takahashi T, Souma Y, Yanagimoto Y, Nagase H, Tanaka K, Miyazaki Y, Makino T, Kurokawa Y, Yamasaki M, Mori M, Doki Y, Nakajima K. 2019. Innovation in surgery/operating room driven by Internet of Things on medical devices. Surgical Endoscopy, 33(10): 3469–3477. https://doi.org/10.1007/s00464-018-06651-4
• Yi PH, Wei J, Kim TK, Sair HI, Hui FK, Hager GD, Fritz J, Oni JK. 2020. Automated detection classification of knee arthroplasty using deep learning. The Knee, 27(2): 535–542. https://doi.org/10.1016/j.knee.2019.11.020.