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Biomedical subjects

Eytan Blumenthal

Publications and source records attributed to Eytan Blumenthal.

2 recordsLinked to original sources

[The medical treatment of glaucoma].

Chronic open angle glaucoma is a one of the leading causes of irreversible blindness. Elevated intraocular pressure is a major risk factor for the progression of this disease. At present, most patients suffering from open angle glaucoma have started medical therapy. The goal is to reduce the intraocular pressure to an individualized target pressure in an effort to delay the progression of damage to the optic nerve. Until a decade ago, topical beta-adrenergic antagonists, adrenergic agonists, miotics and oral carbonic anhydrase inhibitors comprised the common medications in use. In the past decade many new drugs have been introduced. These drugs exert less systemic side effects and are very effective in lowering the intraocular pressure and furthermore, are easier to comply with. These include: local carbonic anhydrase inhibitors, prostaglandin F2á analogues and á2 adrenergic selective agonists. Due to lack of consensus as to the initial medication of choice for the commencement of treatment, the traditional tendency to initiate treatment with a local beta adrenergic antagonist persists. This review attempts to familiarize the reader with the new arsenal of glaucoma medications.

Adrenergic Agonists↗

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

PURPOSE: To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS: Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs. RESULTS: MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices. CONCLUSIONS: MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.

Diagnosis, Computer-Assisted↗