Hyug-Gi Kim1, Kyung Mi Lee1, Eui Jong Kim1, Jin San Lee2. 1. Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Republic of Korea. 2. Department of Neurology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Republic of Korea.
Abstract
BACKGROUND: Sinus X-ray imaging is still used in the initial evaluation of paranasal sinusitis, which is diagnosed by the opacification or air/fluid level in the sinuses and best seen in the Waters' view of the paranasal sinus (PNS). The objective of this study was to investigate the feasibility of recognizing the maxillary sinusitis features using PNS X-ray images, as well as to propose the most effective method of determining a reasonable consensus using multiple deep learning models. METHODS: A total of 4,860 patients, which included 2,430 normal and maxillary sinusitis subjects each, underwent Waters' view PNS X-ray scan. The datasets were randomly split into training (70%), validation (15%), and test (15%) subsets. We implemented a majority decision algorithm to determine a reasonable consensus using three multiple convolutional neural network (CNN) models: VGG-16, VGG-19, and ResNet-101. The performance of sinusitis detection was evaluated with quantitative accuracy (ACC) and activation maps. RESULTS: We compared the results of our approaches with ACC and activation maps. ACC [and area under the curve (AUC)] of the internal test dataset was evaluated as 87.4% (0.891), 90.8% (0.891), 93.7% (0.937), and 94.1% (0.948) for VGG-16, VGG-19, ResNet-101, and the majority decision, respectively. ACC (and AUC) of the external test dataset was evaluated as 87.58% (0.877), 87.58% (0.877), 92.12% (0.929), and 94.12% (0.942) for VGG-16, VGG-19, ResNet-101, and the majority decision, respectively. Majority decision algorithms can detect missing and correct lesions using a compensation function of the majority decision. CONCLUSIONS: The majority decision algorithm showed high accuracy and significantly more accurate lesion detection compared with those of individual CNN models. The proposed deep learning method with PNS X-ray images can be used as an adjunct to classify maxillary sinusitis.
BACKGROUND: Sinus X-ray imaging is still used in the initial evaluation of paranasal sinusitis, which is diagnosed by the opacification or air/fluid level in the sinuses and best seen in the Waters' view of the paranasal sinus (PNS). The objective of this study was to investigate the feasibility of recognizing the maxillary sinusitis features using PNS X-ray images, as well as to propose the most effective method of determining a reasonable consensus using multiple deep learning models. METHODS: A total of 4,860 patients, which included 2,430 normal and maxillary sinusitis subjects each, underwent Waters' view PNS X-ray scan. The datasets were randomly split into training (70%), validation (15%), and test (15%) subsets. We implemented a majority decision algorithm to determine a reasonable consensus using three multiple convolutional neural network (CNN) models: VGG-16, VGG-19, and ResNet-101. The performance of sinusitis detection was evaluated with quantitative accuracy (ACC) and activation maps. RESULTS: We compared the results of our approaches with ACC and activation maps. ACC [and area under the curve (AUC)] of the internal test dataset was evaluated as 87.4% (0.891), 90.8% (0.891), 93.7% (0.937), and 94.1% (0.948) for VGG-16, VGG-19, ResNet-101, and the majority decision, respectively. ACC (and AUC) of the external test dataset was evaluated as 87.58% (0.877), 87.58% (0.877), 92.12% (0.929), and 94.12% (0.942) for VGG-16, VGG-19, ResNet-101, and the majority decision, respectively. Majority decision algorithms can detect missing and correct lesions using a compensation function of the majority decision. CONCLUSIONS: The majority decision algorithm showed high accuracy and significantly more accurate lesion detection compared with those of individual CNN models. The proposed deep learning method with PNS X-ray images can be used as an adjunct to classify maxillary sinusitis.
Authors: Mark Cicero; Alexander Bilbily; Errol Colak; Tim Dowdell; Bruce Gray; Kuhan Perampaladas; Joseph Barfett Journal: Invest Radiol Date: 2017-05 Impact factor: 6.016
Authors: Claudia F E Kirsch; Julie Bykowski; Joseph M Aulino; Kevin L Berger; Asim F Choudhri; David B Conley; Michael D Luttrull; Diego Nunez; Lubdha M Shah; Aseem Sharma; Vilaas S Shetty; Rathan M Subramaniam; Sophia C Symko; Rebecca S Cornelius Journal: J Am Coll Radiol Date: 2017-11 Impact factor: 5.532
Authors: David B Larson; Matthew C Chen; Matthew P Lungren; Safwan S Halabi; Nicholas V Stence; Curtis P Langlotz Journal: Radiology Date: 2017-11-02 Impact factor: 11.105
Authors: Antonio Mario Bulfamante; Francesco Ferella; Austin Michael Miller; Cecilia Rosso; Carlotta Pipolo; Emanuela Fuccillo; Giovanni Felisati; Alberto Maria Saibene Journal: Eur Arch Otorhinolaryngol Date: 2022-10-19 Impact factor: 3.236
Authors: Taseef Hasan Farook; Nafij Bin Jamayet; Johari Yap Abdullah; Mohammad Khursheed Alam Journal: Pain Res Manag Date: 2021-04-24 Impact factor: 3.037