Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans

Quek, Ten Cheer; Takahashi, Kengo; Kang, Hyun Goo; Thakur, Sahil; Deshmukh, Mihir; Tseng, Rachel Marjorie Wei Wen; Nguyen, Ha H.; Tham, Yih‐Chung; Rim, Tyler Hyungtaek; Kim, Sung Soo; Yanagi, Yasuo; Liew, Gerald; Cheng, Ching-Yu

doi:10.1007/s13167-022-00301-5

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…17 Recent studies have shown that AI-based techniques can be very useful to prevent eye diseases via early detection before the disease progresses to a pathological condition. 18,19 Also, AI/ML-based pattern analysis methods applied to the protein proles can give diagnostic results with a high degree of sensitivity and specicity.…”

Section: Introductionmentioning

confidence: 99%

Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome

Adigal

Bhandary

Hegde³

et al. 2023

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome

Adigal

Bhandary

Hegde³

et al. 2023

RSC Adv.

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“…The accurate segmentation and quantification of retinal fluid can help to monitor the state of disease and provide research approaches for precision medicine. Quek et al [ 25 ] proposed a DL-based AI application which provides personalized, continuous monitoring for patients at risk of retinal fluid complications by quantifying the total fluid area in OCT B-scan images. In addition, CMFV quantification can alert patients and clinicians to any dangerous increase in fluid so that steps can be taken to prevent further deterioration and progression.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images

Ye,

Gao,

et al. 2023

Ophthalmol Ther

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Introduction We studied the correlation of central macular fluid volume (CMFV) and central subfield thickness (CST) with best-corrected visual acuity (BCVA) in treatment-naïve eyes with diabetic macular edema (DME) 1 month after anti-vascular endothelial growth factor (VEGF) therapy. Methods This retrospective cohort study investigated eyes that received anti-VEGF therapy. All participants underwent comprehensive examinations and optical coherence tomography (OCT) volume scans at baseline (M0) and 1 month after the first treatment (M1). Two deep learning models were separately developed to automatically measure the CMFV and the CST. Correlations were analyzed between the CMFV and the logMAR BCVA at M0 and logMAR BCVA at M1. The area under the receiver operating characteristic curve (AUROC) of CMFV and CST for predicting eyes with BCVA 20/40 at M1 was analyzed. Results This study included 156 DME eyes from 89 patients. The median CMFV decreased from 0.272 (0.061–0.568) at M0 to 0.096 (0.018–0.307) mm 3 at M1. The CST decreased from 414 (293–575) to 322 (252–430) μm. The logMAR BCVA decreased from 0.523 (0.301–0.817) to 0.398 (0.222–0.699). Multivariate analysis demonstrated that the CMFV was the only significant factor for logMAR BCVA at both M0 ( β = 0.199, p = 0.047) and M1 ( β = 0.279, p = 0.004). The AUROC of CMFV for predicting eyes with BCVA 20/40 at M1 was 0.72, and the AUROC of CST was 0.69. Conclusions Anti-VEGF therapy is an effective treatment for DME. Automated measured CMFV is a more accurate prognostic factor than CST for the initial anti-VEGF treatment outcome of DME. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-023-00746-5.

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Using deep learning algorithms for built-up area extraction from high-resolution GÖKTÜRK-1 satellite imagery

ARIKAN İSPİR,

YILDIZ

2024

Earth Sci Inform

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Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans

Cited by 6 publications

References 42 publications

Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome

Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome

Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images

Using deep learning algorithms for built-up area extraction from high-resolution GÖKTÜRK-1 satellite imagery

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