Peripheral Cornea Crosslinking Before Deep Anterior Lamellar Keratoplasty

Since Cornea crosslinking (CXL) has been proven to halt progression and biomechanically stabilize keratoconus, we hypothesized that CXL of the corneal periphery 3 months prior to corneal transplantation can reduce the incidence of recurrent ectasia by strengthening the peripheral corneal tissue and causing apoptosis of diseased peripheral host keratocytes. Thus, the aim of this case-repot was to propose a novel peripheral CXL technique prior to keratoplasty and evaluate its safety. A 22-year-old woman was admitted with advanced right keratoconus and corrected distance visual acuities of 20/30 in the right eye and 20/200 in the left eye with a manifest refraction of -3.00 -8.00 × 36 and -17.00 -11.50 × 90, respectively. The proposed treatment involved crosslinking of peripheral corneal tissue (6.5-9.5mm), sparing the central cornea and limbus, three months prior to corneal transplantation as a means of biomechanically strengthening the peripheral cornea tissue. We performed peripheral CXL technique in a patient with keratoconus undergoing deep anterior lamellar keratoplasty (DALK). This procedure was feasible and safe with repopulation of the peripheral cornea with keratocytes and no significant endothelial cell loss. This method might reduce or eliminate the need for repeat corneal transplantation in patients with recurrent ectasia. Further studies are needed to confirm the results

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.</jats:sec