# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 1 Introduction  
In today's rapidly advancing era of technology and automation, the healthcare industry is actively exploring innovative solutions to enhance patient care and improve diagnostic practices. One area that holds immense potential is the automated analysis of medical diagnostic videos. By leveraging deep learning techniques, these videos can be processed and analyzed in a manner that significantly enhances the efficiency and accuracy of diagnosis. Deep learning has emerged as a powerful tool for analyzing medical videos, enabling automated tasks such as classification, detection, and segmentation. The application of deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, has demonstrated remarkable success in extracting meaningful information from medical videos. These models have been employed to address various challenges, including the classification of different types of medical videos and the segmentation of specific anatomical structures.  
There are two types of medical diagnostic videos: ultrasound and endoscopy. Cardiac ultrasound is known as echocardiography, and we refer to echocardiography as a third type of medical diagnostic video for the rest of this research work. Several researchers [1] have exploited traditional and machine learning techniques to analyze medical videos,such as echocardiographic videos. The analysis tasks include segmentation of left ventricle (LV), myocardium [2], and anterior mitral leaflet [3] from echocardiographic videos. The researchers also apply machine learning techniques for classifying informative frames from endoscopy videos [4]. Other traditional techniques such as naive Bayesian [5], sliding windows Gauss-Seidel [6], and the polar active contour model [7] are explored to perform different operations on ultrasound videos to make diagnosis easy for medical practitioners. However, such research articles are out of the scope of this research. We have only reviewed the application of deep learning techniques for medical video analysis.  
This article presents a detailed review of medical diagnostic video types and deep learning methods applied for their analysis. We have several contributions. First, to the best of our knowledge, this is the first work that comprehensively provides details about the study of all diagnostic videos. Second, we have thoroughly reviewed many research articles on deep-learning-based medical video analysis. Third, current publications are discussed along with their datasets, purpose, performance, methods, and limitations. Fourthly, we identified which deep learning models would be suitable for classifying, segmenting, and detecting clinical videos. Last, we discussed challenges and future research directions for fellow researchers and practitioners.  
The paper is structured as follows: Sections 2 and 3 discuss the methodology and taxonomy followed for this study. Section 4 defines and explains the different deep learning techniques used for extracting spatial and temporal data from medical videos. Section 5 discusses publicly available datasets for medical diagnostic videos. Sections 6-8 give a detailed literature review of deep learning techniques applied to echocardiography, endoscopy, and ultrasound videos. The literature review for each video type is divided into three subsections: Classification, Segmentation, and Detection. In Section 9, we discuss and provide our opinion on methods used for medical video analysis. In Sections 10 and 11, the challenges, future implications, and conclusions for deep learning and medical video analysis are provided.