RESUMO
A mature, hypermature, or white cataract needs posterior segment evaluation before surgery for prognostification. Ultrasonography is the preferred method for this. White cataract or intumescent cataract risks intraoperative capsulorhexis running out leading to devastating complications due to high intralenticular pressure. Altghough sligh-lamp examination before surgery can gives clue regarding fluid pockets under anterior capsule in these types of cataract, fluid in posterior compartment of lens can be detected by ultrasonography. The author here described a new sign, the double peak sign which can predict the high intra-lenticular pressure especially in posterior compartment of the lens. So if it detected before surgery, all precautions can be taken during surgery to prevent complications. The A-scan overlay on the B-scan in ultrasonography can detect the hypoechoic area corresponding to the lequified cortex in between solid lens nucleus and posterior capsule, so giving rise to the double peak sign which is described here as an innovation.
Assuntos
Catarata , Ultrassonografia , Humanos , Ultrassonografia/métodos , Prognóstico , Extração de Catarata/efeitos adversos , Extração de Catarata/métodos , Masculino , Feminino , Cuidados Pré-Operatórios/métodos , Pessoa de Meia-IdadeRESUMO
PURPOSE: To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR. METHODS: The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed. RESULTS: 2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively. CONCLUSIONS AND RELEVANCE: Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.
Assuntos
Diabetes Mellitus , Retinopatia Diabética , Humanos , Inteligência Artificial , Retinopatia Diabética/diagnóstico , Fundo de Olho , Programas de Rastreamento/métodos , Retina , Estudos Retrospectivos , Método Duplo-CegoRESUMO
Hidrocystomas are rare benign cystic tumors of the sweat glands, with no cases of hidrocystoma of nasolacrimal duct previously reported. We present a case of a 28-year-old female with no prior significant medical history presenting with an enlarging mass medial to her right lower eyelid for 2 years. Computed tomography scan showed nodular soft tissue in the middle part of the right nasolacrimal duct. Following complete excision of the cyst, histopathology reported an apocrine hidrocystoma, which is very rare.