[Research on the classification model of chronic sinusitis based on VGG].

Objective:To build a VGG-based computer-aided diagnostic model for chronic sinusitis and evaluate its efficacy. Methods:①A total of 5 000 frames of diagnosed sinus CT images were collected. The normal group consisted of 1 000 frames（250 frames each of maxillary sinus, frontal sinus, septal sinus, and pterygoid sinus）, while the abnormal group consisted of 4 000 frames（1 000 frames each of maxillary sinusitis, frontal sinusitis, septal sinusitis, and pterygoid sinusitis）. ②The models were trained and simulated to obtain five classification models for the normal group, the pteroid sinusitis group, the frontal sinusitis group, the septal sinusitis group and the maxillary sinusitis group, respectively. The classification efficacy of the models was evaluated objectively in six dimensions: accuracy, precision, sensitivity, specificity, interpretation time and area under the ROC curve（AUC）. ③Two hundred randomly selected images were read by the model with three groups of physicians（low, middle and high seniority） to constitute a comparative experiment. The efficacy of the model was objectively evaluated using the aforementioned evaluation indexes in conjunction with clinical analysis. Results:①Simulation experiment: The overall recognition accuracy of the model is 83.94%, with a precision of 89.52%, sensitivity of 83.94%, specificity of 95.99%, and the average interpretation time of each frame is 0.2 s. The AUC for sphenoid sinusitis was 0.865（95%CI 0.849-0.881）, for frontal sinusitis was 0.924（0.991-0.936）, for ethmoidoid sinusitis was 0.895（0.880-0.909）, and for maxillary sinusitis was 0.974（0.967-0.982）. ②Comparison experiment: In terms of recognition accuracy, the model was 84.52%, while the low-seniority physicians group was 78.50%, the middle-seniority physicians group was 80.50%, and the seniority physicians group was 83.50%; In terms of recognition accuracy, the model was 85.67%, the low seniority physicians group was 79.72%, the middle seniority physicians group was 82.67%, and the high seniority physicians group was 83.66%. In terms of recognition sensitivity, the model was 84.52%, the low seniority group was 78.50%, the middle seniority group was 80.50%, and the high seniority group was 83.50%. In terms of recognition specificity, the model was 96.58%, the low-seniority physicians group was 94.63%, the middle-seniority physicians group was 95.13%, and the seniority physicians group was 95.88%. In terms of time consumption, the average image per frame of the model is 0.20 s, the average image per frame of the low-seniority physicians group is 2.35 s, the average image per frame of the middle-seniority physicians group is 1.98 s, and the average image per frame of the senior physicians group is 2.19 s. Conclusion:This study demonstrates the potential of a deep learning-based artificial intelligence diagnostic model for chronic sinusitis to classify and diagnose chronic sinusitis; the deep learning-based artificial intelligence diagnosis model for chronic sinusitis has good classification performance and high diagnostic efficacy.

[Clinical classification of chronic odontogenic maxillary sinusitis]. / K voprosu o klinicheskoi klassifikatsii khronicheskogo odontogennogo gaimorita.

Examination of 248 patients with odontogenic maxillary sinusitis (OMS) has shown that most of OMS forms run a chronic course. Their clinical classification is outlined with a special emphasis on the latent form which occurs in 16.5% of the examinees. An open form of OMS was diagnosed in 51.6% of the patients. Treatment of chronic OMS is surgical in many cases: maxillary sinusotomy with upper jaw teeth examination and treatment.

Saccharin test of maxillary sinus mucociliary function after endoscopic sinus surgery.

OBJECTIVES: To determine the usefulness of the saccharin time (ST) test for evaluating the mucociliary function of the maxillary sinus after endoscopic sinus surgery (ESS) for chronic sinusitis. METHODS: This study was conducted on 88 maxillary sinuses of 74 patients after ESS. The maxillary sinus fontanel was broadly opened via the middle meatus using an endoscope, and a saccharin granule was adhered to the bottom of the maxillary sinus mucosa The time until the patient recognized the sweet taste was recorded. Before the ST test, the bilateral maxillary sinuses were classified into the following four groups on the basis of the post-ESS severity of mucosal edema and swelling as revealed by endoscopic observation: normal (45 sinuses), mild mucosal edema and swelling (24), moderate mucosal finding (14), and severe mucosal finding or filling of the sinus with a polyp(s) (5). RESULTS: The mean ST values in the normal group and the groups with mild, moderate, and severe mucosal edema and swelling were 35.7, 38.1, 63.6, and 88.0 minutes, respectively. Thus the ST increased with the post-ESS severity of the mucosal lesion. However, for the group with mild mucosal edema and swelling, scanning electron microscopic observation of three maxillary sinuses in which the ST exceeded 120 minutes and four sinuses in which the ST was 40 minutes revealed extensive cilia loss in the former sinuses, but not in the latter. A second post-ESS endoscopic observation was performed in 17 patients, revealing improvement in 11 sinuses, no change in 5 sinuses, and aggravation in 1 sinus (compared with the initial test). The ST test was also repeated, revealing that the ST became shorter in most of the endoscopically improved sinus group. However, a few sinuses showed a discrepancy between the change in the endoscopic findings and the ciliary function (ST). CONCLUSION: Measurement of the maxillary sinus ST is a simple, accurate, and useful technique for assessing the post-ESS mucociliary function in conjunction with endoscopy, and the information gained can help in deciding subsequent therapy.

Analysis of maxillary sinusitis using computed tomography.

Odontogenic maxillary sinusitis can be defined as sinusitis induced by a dental lesion. We examined the CT findings of 68 patients with maxillary sinusitis in order to differentiate between inflammation of sinus origin and inflammation of dental origin. Maxillary sinusitis was classified into four types according to clinical symptoms, history and conventional radiographic findings: type 1, simple sinusitis; type 2, odontogenic sinusitis; type 3, mixed sinusitis; type 4, slight sinus abnormality with a dental lesion. The relationship between the type of maxillary sinusitis and CT findings was analysed. Type 1 sinusitis exhibited severe pathological changes in both mucosa and bone which often extended into the nasal cavity and other paranasal sinuses. Type 2 sinusitis exhibited localized pathology on the unilateral antral floor. Type 3 sinusitis exhibited severe pathology characteristic of type 1 combined with type 2 sinusitis. Type 4 sinusitis could be differentiated by the CT findings into type 1 or type 2 sinusitis. The classification of sinusitis in this manner has implications for treatment planning, and CT should therefore be performed when conventional radiography does not provide sufficient information.