[Automated system of the determination of maxillary sinus morphometric parameters]

Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review

Cone-beam computed tomography (CBCT) imaging of the maxillary sinus is indispensable for implantologists, offering three-dimensional anatomical visualization, morphological variation detection, and abnormality identification, all critical for diagnostics and treatment planning in digital implant workflows. The following systematic review presented the current evidence pertaining to the use of artificial intelligence (AI) for CBCT-derived maxillary sinus imaging tasks. An electronic search was conducted on PubMed, Web of Science, and Cochrane up until January 2024. Based on the eligibility criteria, 14 articles were included that reported on the use of AI for the automation of CBCT-derived maxillary sinus assessment tasks. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool was used to evaluate the risk of bias and applicability concerns. The AI models used were designed to automate tasks such as segmentation, classification, and prediction. Most studies related to automated maxillary sinus segmentation demonstrated high performance. In terms of classification tasks, the highest accuracy was observed for diagnosing sinusitis (99.7%), whereas the lowest accuracy was detected for classifying abnormalities such as fungal balls and chronic rhinosinusitis (83.0%). Regarding implant treatment planning, the classification of automated surgical plans for maxillary sinus floor augmentation based on residual bone height showed high accuracy (97%). Additionally, AI demonstrated high performance in predicting gender and sinus volume. In conclusion, although AI shows promising potential in automating maxillary sinus imaging tasks which could be useful for diagnostic and planning tasks in implantology, there is a need for more diverse datasets to improve the generalizability and clinical relevance of AI models. Future studies are suggested to focus on expanding the datasets, making the AI model's source available, and adhering to standardized AI reporting guidelines.

[Diagnostic criteria of maxillary sinus hypoplasia using cone-beam computed tomography]

OBJECTIVE

To evaluate the diagnostic significance of X-ray criteria of maxillary sinus hypoplasia.

MATERIAL AND METHODS

Cone-beam computed tomography (CBCT) data of 553 patients (1006 maxillary sinuses) with dental and ENT pathology from Minsk outpatient clinics, were studied. The morphometric parameters of 23 maxillary sinuses with radiological signs of hypoplasia, as well as orbits on the affected side, were analyzed. The maximum linear dimensions were measured using the tools of the CBCT viewer. The convolutional neural network technology was applied for maxillary sinus semi-automatic segmentation.

RESULTS

The reliable radiological signs of hypoplasia of the maxillary sinus are a two-fold decrease in its height and/or width relative to the corresponding dimensions of the orbit, a high level of location of the inferior wall of the sinus, displacement of its medial wall to the lateral side, anterolateral wall asymmetry in association with unilateral hypoplasia, as well as lateralization of the uncinate process and ethmoid infundibulum with narrowing of ostial passage.

CONCLUSION

In unilateral hypoplasia, the volume of the sinus is reduced by 31-58% compared to the contralateral side.