RESUMEN
PURPOSE: Lacrimal bypass is the creation of a fistula connecting the conjunctiva with the lacrimal sac or nasal cavity. Bypass is indicated in canalicular obstruction or agenesis; sac absence, destruction or prior excision; lacrimal pump failure; or dacryocystorhinostomy failure. We aim to review the various techniques that have been developed over the last century for lacrimal bypass. METHODS: We conducted a comprehensive literature review of techniques which have focused on creating a conduit extending from the conjunctiva or canaliculi to the lacrimal sac, or extending that bypass to the nasal cavity bypass. RESULTS: The main techniques reviewed include canaliculodacryocystorhinostomy, conjunctivodacryocystostomy, conjunctivorhinostomy, conjunctivodacryocystorhinostomy, and conjunctivoductivodacryocystorhinostomy. CONCLUSION: Lacrimal bypass surgery has evolved due to innovation in microsurgical techniques, instruments and materials. Conjunctivodacryocystorhinostomy with Jones tube insertion is the predominant bypass technique, reflecting a culmination of historical developments. Understanding the variety of lacrimal bypass techniques is important for exploring alternative options when necessary.
RESUMEN
PURPOSE: Advances in artificial intelligence (AI)-based named entity extraction (NER) have improved the ability to extract diagnostic entities from unstructured, narrative, free-text data in electronic health records. However, there is a lack of ready-to-use tools and workflows to encourage the use among clinicians who often lack experience and training in AI. We sought to demonstrate a case study for developing an automated registry of ophthalmic diseases accompanied by a ready-to-use low-code tool for clinicians. METHODS: We extracted deidentified electronic clinical records from a single centre's adult outpatient ophthalmology clinic from November 2019 to May 2022. We used a low-code annotation software tool (Prodigy) to annotate diagnoses and train a bespoke spaCy NER model to extract diagnoses and create an ophthalmic disease registry. RESULTS: A total of 123,194 diagnostic entities were extracted from 33,455 clinical records. After decapitalisation and removal of non-alphanumeric characters, there were 5070 distinct extracted diagnostic entities. The NER model achieved a precision of 0.8157, recall of 0.8099, and F score of 0.8128. CONCLUSION: We presented a case study using low-code artificial intelligence-based NLP tools to produce an automated ophthalmic disease registry. The workflow created a NER model with a moderate overall ability to extract diagnoses from free-text electronic clinical records. We have produced a ready-to-use tool for clinicians to implement this low-code workflow in their institutions and encourage the uptake of artificial intelligence methods for case finding in electronic health records.
