RESUMO
Recent investigations have defined the pathophysiological basis of many hereditary ataxias (HAs), including loss-of-function as well as gain-of-function mechanisms at either the RNA or protein level. Preclinical studies have assessed gene editing, gene and protein replacement, gene enhancement, and gene knockdown strategies. Methodologies include viral vector delivery of genes, oligonucleotide therapies, cell-penetrating peptides, synthetic transcription factors, and technologies to deliver therapies to defined targets. In this review, we focus on Friedreich ataxia (FRDA) and the polyglutamine ataxias in which translational research is active. However, much remains to be done to identify safe and effective molecules, create ideal delivery methods, and perform innovative clinical trials to prove the safety and efficacy of treatments for these rare but devastating diseases.
RESUMO
Cataract surgery is one of the most performed procedures worldwide, and cataracts are rising in prevalence in our aging population. With the increasing utilization of artificial intelligence (AI) in the medical field, we aimed to understand the extent of present AI applications in ophthalmic microsurgery, specifically cataract surgery. We conducted a literature search on PubMed and Google Scholar using keywords related to the application of AI in cataract surgery and included relevant articles published since 2010 in our review. The literature search yielded information on AI mechanisms such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) as they are being incorporated into pre-operative, intraoperative, and post-operative stages of cataract surgery. AI is currently integrated in the pre-operative stage of cataract surgery to calculate intraocular lens (IOL) power and diagnose cataracts with slit-lamp microscopy and retinal imaging. During the intraoperative stage, AI has been applied to risk calculation, tracking surgical workflow, multimodal imaging data analysis, and instrument location via the use of "smart instruments". AI is also involved in predicting post-operative complications, such as posterior capsular opacification and intraocular lens dislocation, and organizing follow-up patient care. Challenges such as limited imaging dataset availability, unstandardized deep learning analysis metrics, and lack of generalizability to novel datasets currently present obstacles to the enhanced application of AI in cataract surgery. Upon addressing these barriers in upcoming research, AI stands to improve cataract screening accessibility, junior physician training, and identification of surgical complications through future applications of AI in cataract surgery.
Although barriers remain for the generalizability and standardization of artificial intelligence in cataract surgery, there are growing applications improving efficiency, data analysis, safety, training, and patient outcomes in pre-operative, intraoperative, and post-operative stages.