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Purpose The aim of this study was to evaluate the volumetric characteristics of mucous retention cysts (MRCs) in the maxillary sinus and to analyze potential associations of MRCs with dentoalveolar pathologies. Materials and Methods Cone-beam computed tomography (CBCT) scans exhibiting bilateral maxillary sinuses that were acquired from January 2016 to February 2019 were initially screened. A total of 227 scans (454 sinuses) that fulfilled the inclusion criteria were included. The presence, location, and volumetric characteristics of the diagnosed MRCs were evaluated on CBCT images using the 3D-Slicer software platform. The presence of MRCs was correlated with potential influencing factors including age, sex, and dentoalveolar pathology. For MRCs located on the sinus floor, factors with a potential impact on the volume, surface, and diameter were analyzed. Results An MRC was present in 130 (28.6%) of the 454 sinuses. Most MRCs were located on the sinus walls and floor. The mean MRC volume, surface, and diameter were 551.21±1368.04 mm 3 , 228.09±437.56 mm 2 , and 9.63±5.40 mm, respectively. Significantly more sinuses with associated endodontically treated teeth/periapical lesions were diagnosed with an MRC located on the sinus floor. For MRCs located on the sinus floor, endodontic status exhibited a significant association with increased volume, surface, and diameter. Conclusion Periapical lesions might be a contributing factor associated with the presence and volume of MRCs located on the sinus floor. The 3D-Slicer software platform was found to be a useful tool for clinicians to analyze the size of MRCs before surgical interventions such as sinus floor elevation procedures.

Objectives: The present work describes the status and contents of The Human Bone Collection of the Faculty of Dentistry at the University of Hong Kong. Materials and methods: The Collection originates from the 1980s and became officially established in 2017 for teaching and research purposes. Most of the Collection consists of unclaimed human remains of southern Chinese individuals exhumed from local cemeteries and donated to the Faculty in the last few decades. The demographic information was provided largely from burial records and forensic estimations. Since 2016, the Collection has undergone a process of reorganization into cranial and postcranial remains, followed by preservation procedures that included cleaning and classification. Results: The Collection currently consists of remains belonging to about 368 individuals (243 males, 54 females, 71 unknown), with ages ranging from 0.8 to 90 years (mean 57.4 years). It comprises cranial remains belonging to 260 individuals(169 males, 39 females, 52 unknown), and postcranial remains belonging to 248 individuals (180 males, 42 females, 26 unknown). The preservation status ranges from poor to good, with the cranial remains better preserved than the postcranial elements. For a large number of individuals, ear ossicles, soil samples, and other materials are also available.Discussion: The Collection is accessible to local and international institutions for teaching and research.

Objectives: To investigate the dose-area product (DAP) of cone-beam computed tomography (CBCT) examinations for different scan settings and imaging indications, and to establish institutional diagnostic reference levels (DRLs) for dose optimization. Methods: A retrospective analysis of the DAP values of 3568 CBCT examinations taken from two different devices at the Prince Philip Dental Hospital, Hong Kong between 2016 and 2021 was performed. Patient- (age, gender, and imaging indication) and imaging-related (CBCT device, field-of-view (FOV), and voxel size) were correlated with the DAPs. The indication-oriented third-quartile DAP values were compared with DRLs from the UK, Finland, and Switzerland. The obtained third-quartile DAPs lower than the national DRLs and those for which no national DRLs have been proposed were used to establish institutional DRLs. Results: In the investigated CBCTs, the DAP value for large FOV scans was significantly lower than medium/small FOVs. CBCTs with a small voxel size exhibited a significantly higher DAP than those with a medium/large voxel size. CBCTs for endodontic, periodontal, orthodontic, or orthognathic evaluation exhibited a significantly higher DAP than other indications. Twelve indication-oriented institutional DRLs were established and five of them were lower than the national DRLs: third molars (229 mGy×cm2), jaw cysts/tumors (410 mGy×cm2), maxillary sinus pathology (520 mGy×cm2), developing dentition (164 mGy×cm2), and periapical lesions (564 mGy×cm2). Conclusions: CBCT examinations for endodontic, periodontal, orthodontic, or orthognathic evaluation may deliver a higher radiation dose to the patient than other imaging tasks. A periodic review of the patient dose from CBCT imaging and establishment of institutional DRLs for specific clinical settings are needed for monitoring patient dose and to optimize indication-oriented scanning protocols.

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Machine learning models have been increasingly considered to model head and neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of the disease. As the concept of data-centric artificial intelligence is still incipient in healthcare systems, little is known about the data quality of the models proposed for clinical utility. This is important as it supports the generalizability of the models and data standardization. Therefore, this study overviews the quality of structured and unstructured data used for machine learning model construction in head and neck cancer. Relevant studies reporting on the use of machine learning models based on structured and unstructured custom datasets between January 2016 and June 2022 were sourced from PubMed, EMBASE, Scopus, and Web of Science electronic databases. Prediction model Risk of Bias Assessment (PROBAST) tool was used to assess the quality of individual studies before comprehensive data quality parameters were assessed according to the type of dataset used for model construction. A total of 159 studies were included in the review; 106 utilized structured datasets while 53 utilized unstructured datasets. Data quality assessments were deliberately performed for 14.2% of structured datasets and 11.3% of unstructured datasets before model construction. Class imbalance and data fairness were the most common limitations in data quality for both types of datasets while outlier detection and lack of representative outcome classes were common in structured and unstructured datasets respectively. Furthermore, this review found that class imbalance reduced the discriminatory performance for models based on structured datasets while higher image resolution and good class overlap resulted in better model performance using unstructured datasets during internal validation. Overall, data quality was infrequently assessed before the construction of ML models in head and neck cancer irrespective of the use of structured or unstructured datasets. To improve model generalizability, the assessments discussed in this study should be introduced during model construction to achieve data-centric intelligent systems for head and neck cancer management.