{'fraction_non_alphanumeric': 0.015741507870753936, 'fraction_numerical': 0.004971002485501243, 'mean_word_length': 5.786516853932584, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
[Applicability of artificial neural networks in postoperative hearing improvement prognosis in patients with chronic otitis media].

The primary objective of surgical treatment of chronic otitis media is to remove the pathological changes and restore the biology of the middle ear. Subsequently, ossicular chain reconstruction is performed in order to restore hearing. The aim of the study was to evaluate the efficacy of mathematical models (artificial neural networks) in postoperative hearing improvement prognosis. The models based on preoperative anamnesis and data concerning the operative procedure. The analyzed group comprised of 135 patients operated for chronic otitis media in the Department of Otolaryngology CM UJ in Krakow. The measure of postoperative hearing improvement was cochlear reserve (air-bone gap) closure depending on the type of ossicular chain reconstruction. The artificial neural networks provided 100% correct predictions basing on preoperative clinical examination, pathological changes found in the middle ear and description of the surgical procedure. The study proves that artificial neural networks are effective in functional result prognosis in tympanoplastic surgery.