Diagnostic challenges in a case of an isolated third nerve palsy.
American journal of ophthalmology case reports
2020; 18: 100585
Neuro-ophthalmic manifestations may be the first and sole presenting feature of a nasopharyngeal carcinoma. Peri-neural spread is an emerging phenomenon that explains the distant spread of tumour cells well beyond the local extent of invasion. This under recognized route of tumour spread often results in delayed diagnosis and reduced life expectancy. The authors report a case of an isolated third nerve palsy as the only initial manifestation of nasopharyngeal carcinoma and emphasize the need for a high index of suspicion.The patient presented with left painful pupil involving complete third nerve palsy. Contrast enhanced imaging was initially deferred due to renal impairment. Plain MRI with MRA brain was normal. Hematology was suggestive of giant cell arteritis which is a rare but well documented cause of painful nerve palsies in the elderly. Unresponsiveness to steroids prompted contrast imaging with a reduced gadolinium dosing and hemodialysis backup which finally revealed a nasopharyngeal carcinoma.This report is the journey of a third nerve palsy from a clinical diagnosis of an aneurysm (pupil involving palsy) to a probable diagnosis of giant cell arteritis (based on hematology) and to a final diagnosis of nasopharyngeal carcinoma (based on contrast imaging and immunohistochemistry)Nasopharyngeal carcinoma can be successfully cured if detected early. This report highlights the various manifestations of nasopharyngeal carcinoma and challenges faced in diagnosing this elusive tumor.
View details for DOI 10.1016/j.ajoc.2020.100585
View details for PubMedID 32099933
View details for PubMedCentralID PMC7031130
Artificial Intelligence in Global Ophthalmology: Using Machine Learning to Improve Cataract Surgery Outcomes at Ethiopian Outreaches.
Journal of cataract and refractive surgery
Differences between target and implanted intraocular lens (IOL) power in Ethiopian cataract outreach campaigns were evaluated and machine learning (ML) applied to optimize IOL inventory and minimize avoidable refractive error. Patients from Ethiopian cataract campaigns with available target and implanted IOL records were identified and the diopter difference between the two measured. A gradient descent (an ML algorithm) was used to generate an optimal IOL inventory and measured the model's performance across varying surplus levels.Only 45.6% of patients received their target IOL power and 23.6% received underpowered IOLs with current inventory (50% surplus). The ML-generated IOL inventory ensured that >99.5% of patients received their target IOL when using only 39% IOL surplus.In Ethiopian cataract campaigns, the majority of patients have avoidable postoperative refractive error secondary to suboptimal IOL inventory. Optimizing IOL inventory using our ML model might eliminate refractive error from insufficient inventory and reduce costs.
View details for DOI 10.1097/j.jcrs.0000000000000407
View details for PubMedID 32932371