Search results

The purpose of the paper is to report a case of bilateral inferior iridoschisis who underwent cataract surgery with intraocular lens implantation successfully with the help of iris hooks or pupillary expanders.

A 71-year-old male presented with inferior iridoschisis in both eyes, history of angle closure glaucoma (ACG), cataract and shallow anterior chamber (AC) angles inferiorly. A localized area of iris stroma is cleaved in two with anterior atrophic portion disintegrating into fibrils from the posterior stroma, and muscle layer is termed as iridoschisis. Iridoschisis is a rare condition associated with fibrillary iris degeneration, narrow drainage angles and cataract.

Preoperative and postoperative ocular examination, including visual acuity, intraocular pressure and degrees of iris damage, was evaluated. Cataract surgery was performed under topical anesthesia with flexible iris hooks. There were no intraoperative complications whereas marked corneal edema was shown at immediate postoperative period but subsided completely in two weeks’ time. Visual acuity improved from 20/60 to 20/25.

This case report demonstrates that while iridoschisis care during cataract surgery has been reported to be difficult, cataract extraction was managed using iris hooks.

This paper reports the successful management of cataract in a patient with bilateral inferior iridoschisis.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.