The Effect of Toric Intraocular Lens Implantation in Irregular Corneal Steep and Flat Meridian

Purpose. To evaluate the effect of toric intraocular lens implantation in cataract patients with irregular corneal steep and flat meridian. Methods. Data of 112 eyes of 78 patients who underwent toric intraocular lens implantation were analyzed retrospectively. Steep meridian deviations (not 180°) and steep and flat meridian deviations (not 90°) were classified as 0, 1–9, 10–19, 20–29, 30–39, and over 30°. Meridian deviation was measured with a sagittal map of a rotating Scheimpflug camera (Pentacam®: Oculus, Wetzlar, Germany) using PicPickTools (NGWIN, Seoul, Korea). Results. Residual astigmatism (D) of 0 (0.51 ± 0.13, 0.55 ± 0.15) and 1–9 (0.61 ± 0.16, 0.66 ± 0.19) groups were significantly lower than that of 10–19 (0.92 ± 0.24, 0.90 ± 0.28), 20–29 (0.10 ± 0.32, 1.01 ± 0.35), and over 30° groups (1.12 ± 0.37, 1.14 ± 0.40) both in steep meridian deviations and horizontal and vertical meridian deviations at 6 months ( P < 0.05 ). Postoperative mean UCVA (logMAR) of 0 (0.09 ± 0.04, 0.09 ± 0.05) (logMAR) and 1–9 (0.10 ± 0.04, 0.11 ± 0.08) groups was significantly improved compared to that of 10–19 (0.14 ± 0.05, 0.17 ± 0.10), 20–29 (0.18 ± 0.08, 0.21 ± 0.10), and over 30° groups (0.20 ± 0.09, 0.22 ± 0.11) both in steep meridian deviations and horizontal and vertical meridian deviations at 6 months ( P < 0.05 ). Conclusions. Correction of astigmatism with toric intraocular lens implantation is not accurate in corneas with steep meridian deviations and steep and flat meridian deviations of more than 10°. Therefore, care should be taken when we perform toric intraocular lens implantation in patients with irregular corneal meridian.

Repeatability and agreement of white-to-white measurements between slit-scanning tomography, infrared biometry, dual rotating Scheimpflug camera/Placido disc tomography, and swept source anterior segment optical coherence tomography

Purpose To assess the agreement and repeatability of horizontal visible iris diameter (HVID) or white-to-white (WTW) measurements between four imaging modalities; combination slit scanning elevation/Placido tomography, infrared biometry, dual rotating scheimpflug camera/Placido tomography, and swept source anterior segment optical coherence tomography (AS-OCT). Methods A prospective study of 35 right eyes of healthy volunteers were evaluated using the Orbscan IIz, IOL Master 700, Galilei G2, and DRI Triton OCT devices. The inter-device agreement and repeatability of HVID/WTW measurements for each device were analysed. Results Mean HVID/WTW values obtained by the Orbscan IIz, IOL Master 700, Galilei G2 and DRI Triton OCT were 11.77 ± 0.40 mm, 12.40 ± 0.43 mm, 12.25 ± 0.42 mm, and 12.42 ± 0.47 mm, respectively. All pairwise comparisons revealed statistically significant differences in mean HVID/WTW measurements (p = <0.01) except for the IOL Master 700—DRI OCT Triton pair (p = 0.56). Mean differences showed that the DRI Triton OCT produced the highest HVID/WTW values, followed by the IOL Master 700, Galilei G2 and Orbscan IIz, respectively. The limits of agreement were large on all device pairs. There was high repeatability for all devices (ICC ≥ 0.980). The highest repeatability was seen in the Galilei G2 (ICC = 0.995) and lowest in the Orbscan IIz (ICC = 0.980). Conclusions The four devices exhibit high repeatability, but should not be used interchangeably for HVID/WTW measurements in clinical practice.

Keratoconus detection of changes using deep learning of colour-coded maps

ObjectiveTo evaluate the accuracy of convolutional neural networks technique (CNN) in detecting keratoconus using colour-coded corneal maps obtained by a Scheimpflug camera.DesignMulticentre retrospective study.Methods and analysisWe included the images of keratoconic and healthy volunteers’ eyes provided by three centres: Royal Liverpool University Hospital (Liverpool, UK), Sedaghat Eye Clinic (Mashhad, Iran) and The New Zealand National Eye Center (New Zealand). Corneal tomography scans were used to train and test CNN models, which included healthy controls. Keratoconic scans were classified according to the Amsler-Krumeich classification. Keratoconic scans from Iran were used as an independent testing set. Four maps were considered for each scan: axial map, anterior and posterior elevation map, and pachymetry map.ResultsA CNN model detected keratoconus versus health eyes with an accuracy of 0.9785 on the testing set, considering all four maps concatenated. Considering each map independently, the accuracy was 0.9283 for axial map, 0.9642 for thickness map, 0.9642 for the front elevation map and 0.9749 for the back elevation map. The accuracy of models in recognising between healthy controls and stage 1 was 0.90, between stages 1 and 2 was 0.9032, and between stages 2 and 3 was 0.8537 using the concatenated map.ConclusionCNN provides excellent detection performance for keratoconus and accurately grades different severities of disease using the colour-coded maps obtained by the Scheimpflug camera. CNN has the potential to be further developed, validated and adopted for screening and management of keratoconus.