AI-based system for predicting angle dysgenesis in eye of glaucoma patients using Anterior segment spectral domain optical coherence tomography (ASOCT) image, has DL neural network device to train DL-CNN models for consensus-based prediction where training involves earning from provided data
	GUPTA V; GUPTA D; DHAKONIA S B
	2023-10-12
专利权人	INT CENT GENETIC ENG & BIOTECHNOLOGY (ITGE-Non-standard)
申请日期	2023-10-12
专利号	IN202311068626-A
成果简介	NOVELTY - The AI-based system (100) has a smart interactive screen for user-friendly operation by the operator (120). An image processing device (102) receives the raw Anterior segment spectral domain optical coherence tomography (ASOCT) image scanned from the ASOCT machine (110) and processing the raw ASOCT image scanned into the processed image scan. The image processing device comprises the image cropping unit (106) to first crop the raw ASOCT image scans into two datasets of cropped images. The first cropped dataset includes cropped images with Iridocorneal Angle (ICA) area and a second cropped dataset includes the cropped images with Trabecular Meshwork (TM) area of an eye. The image augmentation unit (108) applies two datasets of cropped images to incorporate variability that exists in real, practical settings and to increase the total number of images in the two datasets of cropped images to obtain two datasets of augmented images. A deep learning (DL) neural network device (104) trains three DL-convolutional neural network (CNN) models (116) for consensus-based prediction where the training involves earning from the provided data. USE - AI-based system for predicting angle dysgenesis in eye of glaucoma patients using Anterior segment spectral domain optical coherence tomography (ASOCT) images. ADVANTAGE - The system ensures resource-intensive genetic testing and streamlining cost-effective patient management, and can empower healthcare workers to make well-informed referrals by optimizing patient care pathways by referring the patient to the proper specialized outpatient departments (OPD), at the right time and also reducing unnecessary referrals. The system enables timely and informed decision-making regarding the appropriate treatment intervention whether laser or surgical treatment should be given to the patient. DETAILED DESCRIPTION - An INDEPENDENT CLAIM is included for an AI-based method for prediction of angle dysgenesis in the eyes of glaucoma patients. DESCRIPTION OF DRAWING(S) - The drawing shows a block diagram of AI-based system. 100AI-based system 102image processing device 104deep learning (DL) neural network device 106image cropping unit 108image augmentation unit 110ASOCT machine
IPC 分类号	A61P-027/06 ; G06N-003/04 ; G06N-003/08 ; G16H-040/20 ; G16H-050/20
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/20011
专题	中国科学院新疆生态与地理研究所
作者单位	INT CENT GENETIC ENG & BIOTECHNOLOGY (ITGE-Non-standard)
推荐引用方式 GB/T 7714	GUPTA V,GUPTA D,DHAKONIA S B. AI-based system for predicting angle dysgenesis in eye of glaucoma patients using Anterior segment spectral domain optical coherence tomography (ASOCT) image, has DL neural network device to train DL-CNN models for consensus-based prediction where training involves earning from provided data. IN202311068626-A[P]. 2023.

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