Comparison of Ocular Biometric Parameters Between Two Swept-Source Optical Coherence Tomography Devices and Scheimpflug Tomography in Patients with Cataract
INTERNATIONAL JOURNAL OF OPHTHALMOLOGY（2024）
Tianjin Key Laboratory of Retinal Functions and Diseases
Abstract
AIM: To assess and compare the variations and agreements across different ocular biometric parameters using swept-source optical coherence tomography (SS-OCT) and Scheimpflug tomography in patients diagnosed with cataract. METHODS: This prospective case series was conducted at Tianjin Medical University Eye Hospital. In total, 212 eyes from 212 patients scheduled for phacoemulsification were included. Eyes were evaluated preoperatively using two SS-OCT devices (IOLMaster700 and CASIA2) and Scheimpflug tomography (Pentacam). Central corneal thickness (CCT), anterior chamber depth (ACD), aqueous depth (AQD), white-to-white distance (WTW), flat simulated keratometry (Kf), steep simulated keratometry (Ks), mean keratometry (Km), and total corneal keratometry (TKm) were measured. Intraclass correlation coefficient (ICC), 95% confidence intervals (CI) and limits of agreement (LoA) widths were conducted to assess differences and correlations between devices. RESULTS: All parameters, except for Ks, were significantly different. Pairwise comparison revealed no significant differences between keratometry obtained by IOLMaster 700 and Pentacam. LoA widths of all paired comparisons for Ks were >0.80 D. Except for WTW between IOLMaster 700 and CASIA2 and between CASIA2 and Pentacam, other Pearson's coefficients between devices showed a strong correlation (all r>0.95). The ICC of WTW (ICC=0.438, 95%CI 0.167-0.625) showed poor reliability. The reliability of CCT, ACD, and AQD was excellent (all ICC>0.95), whereas that of TKm was good (ICC=0.827, 95%CI 0.221-0.939). A significant linear correlation was also observed among devices. CONCLUSION: The ocular parameters derived from the use of IOLMaster700, CASIA2, and Pentacam exhibit significant discrepancies; as such, measurements from these devices should not be deemed as interchangeable.
MoreTranslated text
Key words
ocular parameters,swept-source optical coherence tomography,cataract
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined