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Background: New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way. Objectives: This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies. Design: A multidimensional comprehensive systematic narrative review. Data sources and methods: A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed. Results: Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes. Conclusion: The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients. Trial registration: The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
Keratoconus: from fundamentals to future Artificial intelligence has changed how we treat the eye disease keratoconus in recent years. This study examines the many keratoconus therapies available, including surgery and contact lens wear, and how artificial intelligence can improve the safety and accuracy of these procedures. We combed through numerous papers to locate this data. To achieve the best outcomes, several parameters and methods should be evaluated. According to the study, some elements from eye scans are more useful than others. The idea behind using artificial intelligence is to help patients see better and treat keratoconus more effectively.
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Purpose To determine if a structured surgical wet laboratory curriculum for ophthalmology residents reduced the rate of posterior capsule rupture (PCR) in phacoemulsification cataract surgery. Setting James A. Haley Veterans' Hospital, Tampa, FL. Design Retrospective cohort study. Methods The study assessed resident-performed phacoemulsification cataract cases from 2011 to 2017, after the creation of a wet laboratory course. Primary outcome measure was PCR. If present, timing of complication, dropped lens fragments, and the need for anterior vitrectomies were noted. Self-reported rates of PCR prior to institution of a wet laboratory course (2010-2011) were compared with cases done by residents who completed the course (2011-2017). Results A total of 3,445 cases were reviewed of which 2.44% (84 cases) noted PCR. Of these, 19% (16) had dropped lens fragments, and 60.7% (51) required anterior vitrectomy. Sixty-nine cases documented timing of PCR with the majority, 58%, occurring during phacoemulsification. When comparing rates of PCR in cases done prior to the presence of a wet laboratory course versus after, there was a significant reduction observed (5.20% before vs. 2.44% after). Conclusion In the presence of a wet laboratory curriculum, the rate of PCR decreased dramatically. The average rate was lower than those reported at other training programs (2.6-9.9%). Most PCR occurred during phacoemulsification, suggesting need for further focused instruction in this step.
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Quaternized triflupromazine derivatives (QTDs) must possess benzyl groups attached to the quaternary nitrogen in order to have significant antitubercular potency. Replacing the quaternary amine with a triazole abolishes antitubercular activity. A modest halogen substitution effect exists, with the 4-bromophenyl QTD 3 having the best selectivity index (>21). All N-benzyl QTDs 1-4 similarly inhibit non-replicating, persistent Mycobacterium tuberculosis with MIC<8 µM, and compounds 1-3 were all nontoxic to mammalian cells in vitro (IC(50)>128 µM).