Dao-Man Xiang1, Nan Jiang1, Li-Hong Chen2, Wen-Si Chen1, Ying Yu1, Jian-Xun Wang1, Ping He1, Wan-Hua Xie1. 1. Department of Ophthalmology, Guangzhou Children's Hospital, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China. 2. Department of Ophthalmology, People's Hospital of Longhua, Shenzhen, China.
Abstract
BACKGROUND: This study sought to develop and validate a lens opacities classification system based on ultrasound biomicroscopy (UBM) imaging to grade pediatric cataracts. METHODS: The study was conducted at Guangzhou Children's Hospital, Guangzhou Women and Children's Medical Center. UBM images of patients at the hospital from September 2013 to November 2014 were used in this study. We summarized the characteristics of lenticular opacification in each of the following 4 zones: the anterior capsule (A); the cortex (C); the nucleus (N); and the posterior capsule (P). The UBM data and intraoperative videos were compared, and sensitivity, specificity, accuracy, and positive and negative predictive values were determined for our Lens Opacities Classification System based on UBM for Pediatric Cataracts (LOCS-UP) detection. Two physicians classified pediatric cataracts (anterior capsule, cortex, and posterior capsule) by extracting 146 images from the UBM database. Patients' data were recorded to calculate the kappa coefficients. The LOCS-UP was developed. RESULTS: Under this standard, all types of pediatric cataracts can be classified and acquired a code by the LOCS-UP. The LOCS-UP had the highest sensitivity (100%) and specificity (98.96%) in naming the anterior capsule and the lowest sensitivity (50%) and specificity (89.59%) in naming the posterior capsule. Its consistency at naming the anterior capsule was satisfactory (Kappa coefficient: 0.70), and it was also able to name the nucleus, cortex, and posterior capsule (0.56, 0.58, and 0.48, respectively). CONCLUSIONS: LOCS-UP could name pediatric cataracts by providing an unique digital encoding, which could reflect characteristics exactly for different local lens anomalies to all kinds of pediatric cataract patients. This method provides detailed and accurate information about Patients' lenses. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: This study sought to develop and validate a lens opacities classification system based on ultrasound biomicroscopy (UBM) imaging to grade pediatric cataracts. METHODS: The study was conducted at Guangzhou Children's Hospital, Guangzhou Women and Children's Medical Center. UBM images of patients at the hospital from September 2013 to November 2014 were used in this study. We summarized the characteristics of lenticular opacification in each of the following 4 zones: the anterior capsule (A); the cortex (C); the nucleus (N); and the posterior capsule (P). The UBM data and intraoperative videos were compared, and sensitivity, specificity, accuracy, and positive and negative predictive values were determined for our Lens Opacities Classification System based on UBM for Pediatric Cataracts (LOCS-UP) detection. Two physicians classified pediatric cataracts (anterior capsule, cortex, and posterior capsule) by extracting 146 images from the UBM database. Patients' data were recorded to calculate the kappa coefficients. The LOCS-UP was developed. RESULTS: Under this standard, all types of pediatric cataracts can be classified and acquired a code by the LOCS-UP. The LOCS-UP had the highest sensitivity (100%) and specificity (98.96%) in naming the anterior capsule and the lowest sensitivity (50%) and specificity (89.59%) in naming the posterior capsule. Its consistency at naming the anterior capsule was satisfactory (Kappa coefficient: 0.70), and it was also able to name the nucleus, cortex, and posterior capsule (0.56, 0.58, and 0.48, respectively). CONCLUSIONS: LOCS-UP could name pediatric cataracts by providing an unique digital encoding, which could reflect characteristics exactly for different local lens anomalies to all kinds of pediatric cataract patients. This method provides detailed and accurate information about Patients' lenses. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Ali Tabatabaei; Mohammad Yaser Kiarudi; Fariba Ghassemi; Sasan Moghimi; Mohammadreza Mansouri; Ahmad Mirshahi; Ahmad Kheirkhah Journal: Am J Ophthalmol Date: 2011-08-20 Impact factor: 5.258