Overview of Deep Learning Methods Used in the Medical Device Industry

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

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