首页|Cairo University Reports Findings in Artificial Intelligence (Artificial intelli gence system for automatic maxillary sinus segmentation on cone beam computed to mography images)
Cairo University Reports Findings in Artificial Intelligence (Artificial intelli gence system for automatic maxillary sinus segmentation on cone beam computed to mography images)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Cairo ,Egypt,by NewsRx correspondents,research stated,"The study aims to develop a n artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in Cone Beam Computed Tomography (CBCT) volumes and to evaluate the performance of this model. In 101 CBCT scans,MS were annotated usi ng the CranioCatch labelling software (Eskisehir,Turkey) The dataset was divide d into three parts: 80 CBCT scans for training the model,11 CBCT scans for mode l validation,and 10 CBCT scans for testing the model." Our news editors obtained a quote from the research from Cairo University,"The model training was conducted using the nnU-Net v2 deep learning model with a lea rning rate of 0.00001 for 1000 epochs. The performance of the model to automatic ally segment the MS on CBCT scans was assessed by several parameters,including F1-score,accuracy,sensitivity,precision,Area Under Curve (AUC),Dice Coeffic ient (DC),95% Hausdorff Distance (95% HD),and Inte rsection over Union (IoU) values. F1-score,accuracy,sensitivity,precision val ues were found to be 0.96,0.99,0.96,0.96 respectively for the successful segm entation of maxillary sinus in CBCT images. AUC,DC,95% HD,IoU v alues were 0.97,0.96,1.19,0.93,respectively."
CairoEgyptAfricaArtificial Intelli genceComputed TomographyEmerging TechnologiesImaging TechnologyMachine L earningTechnology
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