Report : Middle East and Africa Optical Coherence Tomography Angiography Equipment Market Forecast to 2028 - COVID-19 Impact and Regional Analysis By Type (Tabletop Type and Handheld Type), and End User (Hospitals, Clinics, and Others)
The hospitals segment by end user is estimated to lead the market growth during the forecast period.
According to a new market research study of “Middle East and Africa Optical Coherence Tomography Angiography Equipment Market Forecast to 2028 - COVID-19 Impact and Regional Analysis by Type, End User and Country.” The Middle East and Africa optical coherence tomography angiography equipment market is expected to reach US$ 15.47 million by 2028 from US$ 10.72 million in 2021; it is estimated to grow at a CAGR of 5.4% from 2021 to 2028. The report highlights trends prevailing in the Middle East and Africa optical coherence tomography angiography equipment market and the factors driving market along with those that act as hindrances.
The increasing prevalence of eye disorders has encouraged the angiography equipment market players to adopt advanced technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) for the development of advanced OCT imaging devices. The integration of ML and DL to OCT angiography yields insights into new pathologic signals that could benefit the clinical management of retinal diseases. DL techniques are increasingly being used for analyzing medical images. Based on the target application, promising AI-based models developed for ophthalmology have incorporated various types of imaging, which help predict diabetic retinopathy, glaucoma diagnosis, and age-related macular degeneration. Studies suggest that the DL-based analysis of OCT angiography images obtained in the preoperative setting can help create promising decisive support systems. Moreover, OCT angiography image-based evaluation by DL algorithms enables effective disease detection, prognosis prediction, and image quality control. As a result, DL technology incorporation could potentially enhance disease evaluation accuracy and clinical workflow efficiency, along with aiding the early diagnosis of DR. DL-based models are trained with different kinds of input from conventional OCT and OCT angiography images. Advancements in DL have also improved the state-of-the-art OCT angiography image analysis, including retinal layers and avascular area segmentation, and image quality assessment. In addition, image quality control is essential for accurate disease evaluation on OCT angiography images.
In 2019, Kauer et al. proposed an automatic quality assessment network based on Computational Neural Network (CNN) to evaluate the quality of OCTA scans, and the DL-based model achieved an accuracy of 99.5% to classify the image as good, bad, upper, and low quality. The DL methods were employed in many clinical applications of classification tasks, such as DR classification and AMD classification. For AMD classification, Thakoor et al. presented an interesting study in 2021 using a custom-made 3D CNN and a stack of 2D images of retinal layers of interest as input. By using a two-class classification method (NV-AMD vs. healthy), the classification accuracy was found to be quite high (93.4%).
Support vector machine (SVM) is the most common machine learning method found for OCT angiography image classification. It is used for single disease detection, such as DR and glaucoma, and also employed for DR staging. The other classifiers included neural networks (NNs) and a gradient boosting tree, i.e., XGBoost. The ML classification methods were used in all clinical applications, including DR classification and staging, glaucoma classification, AMD classification, and sickle cell retinopathy (SCR) classification. For AMD classification, in 2018, Alfahaid et al. used rotation-invariant, uniform local binary pattern texture feature computed on 184 images coupled with a KNN classifier to obtain a maximum accuracy of 100% considering the choriocapillaris layer and an accuracy of 89% for all layers. In 2021, Abdelsalam et al. presented the most promising results for DR classification using multifractal parameter computation with an SVM classifier that showed an accuracy of 98.5%.
Thus, AI highlighted can potentially help interpret challenging cases and ensure the highest possible diagnostic accuracy. Also, the combined use of AI and OCT angiography will significantly improve the early diagnosis of diabetic changes in retinal disorders.
The Middle East and Africa optical coherence tomography angiography equipment market, based on type, has been bifurcated into handheld type and tabletop type. The Middle East and Africa optical coherence tomography angiography equipment market, based on end user is segmented into hospitals, clinics, and others. Geographically, the Middle East and Africa optical coherence tomography angiography equipment market is divided into UAE, Saudi Arabia, Saudi Africa and Rest of Middle East and Africa.
Carl Zeiss AG, Canon Inc., Michelson Diagnostics Ltd., Alcon Inc., and Topcon Corporation are among the leading companies operating in the Middle East and Africa optical coherence tomography angiography equipment market.
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