PubMed · 10299874
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R D Wilkinson. 1985. New artificial intelligence systems critique physicians.. https://pubmed.ncbi.nlm.nih.gov/10299874/
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BACKGROUND: Evaluation of facial nerve paresis depends on visual assessment and naturally differs from examiner to examiner. An objective measurement instrument is presented. PATIENTS AND METHOD: Facial features are automatically localized by a parametric face model in videos of a face during relaxation and exercises. Gray-level information is analyzed by a special steerable filter and used to identify symmetries. The computer system was tested in 19 individuals. RESULTS: Automatic localization of facial features such as the upper arc of the head and ears was correct in 95%, the eyes in 82%, and the mouth in 73%. Lid paresis was correctly recognized in seven of ten (70%) and oral paresis in 10 of 12 (83%) cases. Unaffected eyelid movements were identified in eight of nine (89%) and healthy oral regions in all seven (100%) cases. CONCLUSION: The computer system presented is able to automatically localize facial features and to identify facial nerve paresis. It is a considerable step toward automatic and objective grading of facial nerve paresis.
Determining the cause of seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.