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The processing of the retrospective data pool for ophthalmology holds huge potential, especially for the research sector. "Big Data" enables medical science to draw conclusions for the future from historical data. Based on the evaluation of such data, algorithms could be trained, for instance, that are capable of making decisions with the help of artificial intelligence. As a result, the medical decision-making process on certain issues could be accelerated, enriched in qualitative and quantitative terms, or even completely be taken over. Ophthalmology is a rapidly evolving field. Due to the multitude of partly automated medical imaging technologies and the predestined accessibility of the eye for such technologies, ophthalmology, similarly to radiology or dermatology, is well suited for artificial intelligence-assisted image data analysis and the frequently associated initiation of diagnosis and therapy. Meanwhile, numerous studies exist based on AI-assisted image data analysis of ophthalmological image data. To the extent that the algorithms filter out results from the data pools by means of calculation rules and are even capable of making independent decisions on the basis of decision trees, the enormous benefit and, simultaneously, the profit for scientific research is quite obvious. Accordingly, it would be desirable to have unrestricted and comprehensive possibility of corresponding data processing of these health data for ophthalmological research. In spite of the potential for ophthalmology, for which there is only fragmentary evidence, the question of practical feasibility arises. In particular, the legal requirements and limits of European and national data protection law must be taken into account, prior to any unreflected processing of personal (health) data. Only by doing so can we circumvent existing obstacles and pitfalls, which can lead to severe fines. Most important are to date the requirements of two legal texts: The General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). This article provides an overview of the relevant legal requirements applicable in the field of ophthalmology and highlights the major pitfalls and implementation requirements., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht./The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

The training of artificial intelligence (AI) is becoming increasingly popular. More and more studies on lamellar keratoplasty are also being published. In particular, the possibility of non-invasive and high-resolution imaging technology of optical coherence tomography predestines lamellar keratoplasty for the application of AI. Although it is technically easy to perform, there are only a few studies on the use of AI to optimise lamellar keratoplasty. The existing studies focus primarily on the prediction probability of rebubbling in DMEK and DSAEK and on their graft adherence, as well as on the formation of a big bubble in DALK. In addition, the automated recording of routine parameters such as corneal oedema, endothelial cell density or the size of the graft detachment is now possible using AI. The optimisation of lamellar keratoplasty using AI holds great potential. Nevertheless, there are limitations to the published algorithms, in that they can only be transferred between centres, surgeons and different device manufacturers to a limited extent., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht./The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Purpose: The aim of this study was to assess the long-term outcome of excimer laser phototherapeutic keratectomy (PTK) for treating remaining anterior corneal opacities after Descemet membrane endothelial keratoplasty (DMEK)., Methods: This cohort study analyzed 67 eyes of 66 patients undergoing PTK in addition to and after DMEK between 2012 and 2021 at the Department of Ophthalmology, University of Cologne. Patients who were treated by PTK after previous DMEK on the same eye were included. The outcome parameters were best corrected visual acuity (BCVA), changes in refraction, endothelial cell density, corneal densitometry, central corneal thickness, and total corneal higher order aberrations. Patients with visual limitations in addition to anterior opacification were analyzed separately for their visual outcome. Eyes with severe visual limitations that have a very limited visual potential were excluded from the study. In addition, patients with not at least 1 postoperative follow-up examination were excluded from the study., Results: In this study, 67 eyes of 66 patients met all inclusion criteria. The median follow-up period was 13.1 (quartiles: 4.1; 30.7; interquartile range 26.6) months. The mean (±SD) BCVA improved from (logarithm of the minimum angle of resolution) 0.54 (±0.32) to 0.38 (±0.27) at the first postoperative visit ( P < 0.001) and remained significantly improved at all follow-up examinations compared with preoperative BCVA. Refraction and endothelial cell density did not change significantly after PTK. There was a significant decrease in corneal backscatter in the anterior corneal layer ( P < 0.001), and the total higher order aberration ( P = 0.02) and central corneal thickness were significantly reduced at the most recent postoperative visit ( P < 0.001). No immune reactions were observed., Conclusions: Excimer laser PTK after DMEK for persisting anterior corneal opacities has the potential to significantly improve patients' vision without affecting refractive parameters or endothelial cell density., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)