AUTHOR=Ahn Hyunmin , Jun Ikhyun , Seo Kyoung Yul , Kim Eung Kweon , Kim Tae-im 

TITLE=Artificial Intelligence for the Estimation of Visual Acuity Using Multi-Source Anterior Segment Optical Coherence Tomographic Images in Senile Cataract

JOURNAL=Frontiers in Medicine

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.871382

DOI=10.3389/fmed.2022.871382

ISSN=2296-858X

ABSTRACT=<sec><title>Purpose</title><p>To investigate an artificial intelligence (AI) model performance using multi-source anterior segment optical coherence tomographic (OCT) images in estimating the preoperative best-corrected visual acuity (BCVA) in patients with senile cataract.</p></sec><sec><title>Design</title><p>Retrospective, cross-instrument validation study.</p></sec><sec><title>Subjects</title><p>A total of 2,332 anterior segment images obtained using swept-source OCT, optical biometry for intraocular lens calculation, and a femtosecond laser platform in patients with senile cataract and postoperative BCVA ≥ 0.0 logMAR were included in the training/validation dataset. A total of 1,002 images obtained using optical biometry and another femtosecond laser platform in patients who underwent cataract surgery in 2021 were used for the test dataset.</p></sec><sec><title>Methods</title><p>AI modeling was based on an ensemble model of Inception-v4 and ResNet. The BCVA training/validation dataset was used for model training. The model performance was evaluated using the test dataset. Analysis of absolute error (AE) was performed by comparing the difference between true preoperative BCVA and estimated preoperative BCVA, as ≥0.1 logMAR (AE<sub>≥0.1</sub>) or &lt;0.1 logMAR (AE<sub> &lt;0.1</sub>). AE<sub>≥0.1</sub> was classified into underestimation and overestimation groups based on the logMAR scale.</p></sec><sec><title>Outcome Measurements</title><p>Mean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE), and correlation coefficient between true preoperative BCVA and estimated preoperative BCVA.</p></sec><sec><title>Results</title><p>The test dataset MAE, RMSE, and MPE were 0.050 ± 0.130 logMAR, 0.140 ± 0.134 logMAR, and 1.3 ± 13.9%, respectively. The correlation coefficient was 0.969 (<italic>p</italic> &lt; 0.001). The percentage of cases with AE<sub>≥0.1</sub> was 8.4%. The incidence of postoperative BCVA &gt; 0.1 was 21.4% in the AE<sub>≥0.1</sub> group, of which 88.9% were in the underestimation group. The incidence of vision-impairing disease in the underestimation group was 95.7%. Preoperative corneal astigmatism and lens thickness were higher, and nucleus cataract was more severe (<italic>p</italic> &lt; 0.001, 0.007, and 0.024, respectively) in AE<sub>≥0.1</sub> than that in AE<sub> &lt;0.1</sub>. The longer the axial length and the more severe the cortical/posterior subcapsular opacity, the better the estimated BCVA than the true BCVA.</p></sec><sec><title>Conclusions</title><p>The AI model achieved high-level visual acuity estimation in patients with senile cataract. This quantification method encompassed both visual acuity and cataract severity of OCT image, which are the main indications for cataract surgery, showing the potential to objectively evaluate cataract severity.</p></sec>