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Automated Classification and Detection of Staphyloma with Ultrasound Images in Pathologic Myopia Eyes

      Abstract

      The aim of this study was to develop an eyewall curvature- and axial length (AxL)–based algorithm to automate detection (clinician-free) of staphyloma ridge and apex locations using ultrasound (US). Forty-six individuals (with emmetropia, high myopia or pathologic myopia) were enrolled in this study (AxL range: 22.3–39.3 mm), yielding 130 images in total. An intensity-based segmentation algorithm automatically tracked the posterior eyewall, calculating the posterior eyewall local curvature (K) and distance (L) to the transducer and the location of the staphyloma apex. By use of the area under the receiver operator characteristic (AUROC) curve to evaluate the diagnostic ability of eight local statistics derived from K, L and AxL, the algorithm successfully quantified non-uniformity of eye shape with an AUROC > 0.70 for most K-based parameters. The performance of binary classification (staphyloma absence vs. presence) was assessed with the best classifier (the combination of AxL, standard deviation of K and standard deviation of L) yielding a diagnostic validation performance of 0.897, which was comparable to the diagnostic performance of junior clinicians. The staphyloma apex was localized with an average error of 1.35 ± 1.34 mm. Combined with the real-time data acquisition capabilities of US, this method can be employed as a screening tool for clinician-free in vivo staphyloma detection.

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