首页|Nanjing Institute of Technology Reports Findings in Machine Learning (Machine le arning-based decision support system for orthognathic diagnosis and treatment pl anning)
Nanjing Institute of Technology Reports Findings in Machine Learning (Machine le arning-based decision support system for orthognathic diagnosis and treatment pl anning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Nanjing, Peopl e's Republic of China, by NewsRx journalists, research stated, "Dento-maxillofac ial deformities are common problems. Orthodontic-orthognathic surgery is the pri mary treatment but accurate diagnosis and careful surgical planning are essentia l for optimum outcomes." Financial supporters for this research include Key Research and Development Prog ram of Science and Technology Department of Sichuan Province, Research and Devel op Program West China Hospital of Stomatology Sichuan University. The news reporters obtained a quote from the research from the Nanjing Institute of Technology, "This study aimed to establish and verify a machine learning-bas ed decision support system for treatment of dento-maxillofacial malformations. P atients (n = 574) with dento-maxillofacial deformities undergoing spiral CT duri ng January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were co mpared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm w as employed to formulate the orthognathic surgical plan, and subsequently evalua ted by maxillofacial surgeons in a cohort of 50 patients. The objective evaluati on included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepanci es in postoperative cephalometric analysis outcomes. The binary relevance extrem e gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; t he exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and w as improved after human-computer interaction. There was no statistically signifi cant difference between the actual and AI- groups."
NanjingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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