Quality of life and psychological evaluation of patients after anaplastology.

A comprehensive questionnaire with 43 questions was designed to evaluate quality of life, based on rehabilitation with a facial prosthesis. Each patient's psychological situation was assessed using the validated questionnaire and associated scales. Different patient groups were compared with each other in terms of questionnaire scores and general data. In total, 76 patients with a prosthesis of the orbit, nose, or ear, or a combination thereof, were included. There was a highly significant difference in overall satisfaction with defect reconstruction via a prosthesis of the ear compared with the orbit and nose (F(3) = 6.511, p = 0.001). Patients with congenital defects showed a significantly higher level of general satisfaction compared with patients with acquired defects (F(2) = 5.795, p = 0.001). Patients who returned to work were significantly more satisfied with their quality of life (T(57) = 2.626, p = 0.011). With regard to improvements to the state-of-the-art prostheses, the majority of patients suggested better retention, more durable colors, make-up possibilities, less noticeable margins, softer materials, and a movable orbital prosthesis. Within the limitations of the study it seems that facial epitheses improved mental wellbeing and increased quality of life among patients with facial defects. Multiple factors, such as type of facial defect, location of residence, and education can have a potential influence on the quality of life of affected patients. However, further studies are needed, since the psychological and social challenges remain.

Detection of oral squamous cell carcinoma in clinical photographs using a vision transformer.

Oral squamous cell carcinoma (OSCC) is amongst the most common malignancies, with an estimated incidence of 377,000 and 177,000 deaths worldwide. The interval between the onset of symptoms and the start of adequate treatment is directly related to tumor stage and 5-year-survival rates of patients. Early detection is therefore crucial for efficient cancer therapy. This study aims to detect OSCC on clinical photographs (CP) automatically. 1406 CP(s) were manually annotated and labeled as a reference. A deep-learning approach based on Swin-Transformer was trained and validated on 1265 CP(s). Subsequently, the trained algorithm was applied to a test set consisting of 141 CP(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved a classification accuracy of 0.986 and an AUC of 0.99 for classifying OSCC on clinical photographs. Deep learning-based assistance of clinicians may raise the rate of early detection of oral cancer and hence the survival rate and quality of life of patients.

Detection of mandibular fractures on panoramic radiographs using deep learning.

Mandibular fractures are among the most frequent facial traumas in oral and maxillofacial surgery, accounting for 57% of cases. An accurate diagnosis and appropriate treatment plan are vital in achieving optimal re-establishment of occlusion, function and facial aesthetics. This study aims to detect mandibular fractures on panoramic radiographs (PR) automatically. 1624 PR with fractures were manually annotated and labelled as a reference. A deep learning approach based on Faster R-CNN and Swin-Transformer was trained and validated on 1640 PR with and without fractures. Subsequently, the trained algorithm was applied to a test set consisting of 149 PR with and 171 PR without fractures. The detection accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an F1 score of 0.947 and an AUC of 0.977. Deep learning-based assistance of clinicians may reduce the misdiagnosis and hence the severe complications.

New and innovative biomaterials, techniques and therapy concepts: Biologization in maxillofacial surgery, oral surgery and dentistry is in full swing. PRF, PRGF, PRP, blood plasma-stabilized augmentations, supplementation of micronutrients and vitamins - what opportunities do such "biological" approaches actually offer? We introduce them here.

Biomaterials of natural origin have recently gained increasing attention in the field of dental implantology. The requirements for such materials, however, are very high. In addition to high clinical efficiency in tissue regeneration, wound healing should be demonstrably positively influenced. The translational division for regenerative orofacial medicine of the Clinic and Polyclinic for Oral and Maxillofacial Surgery of the University Medical Center Hamburg-Eppendorf (UKE) is examining this research topic by investigating which innovative treatment methods for the reconstruction of bone defects or for augmentative procedures can be applied in the future or are already being applied in the field of oral and maxillofacial surgery.

Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning.

The detection and classification of cystic lesions of the jaw is of high clinical relevance and represents a topic of interest in medical artificial intelligence research. The human clinical diagnostic reasoning process uses contextual information, including the spatial relation of the detected lesion to other anatomical structures, to establish a preliminary classification. Here, we aimed to emulate clinical diagnostic reasoning step by step by using a combined object detection and image segmentation approach on panoramic radiographs (OPGs). We used a multicenter training dataset of 855 OPGs (all positives) and an evaluation set of 384 OPGs (240 negatives). We further compared our models to an international human control group of ten dental professionals from seven countries. The object detection model achieved an average precision of 0.42 (intersection over union (IoU): 0.50, maximal detections: 100) and an average recall of 0.394 (IoU: 0.50-0.95, maximal detections: 100). The classification model achieved a sensitivity of 0.84 for odontogenic cysts and 0.56 for non-odontogenic cysts as well as a specificity of 0.59 for odontogenic cysts and 0.84 for non-odontogenic cysts (IoU: 0.30). The human control group achieved a sensitivity of 0.70 for odontogenic cysts, 0.44 for non-odontogenic cysts, and 0.56 for OPGs without cysts as well as a specificity of 0.62 for odontogenic cysts, 0.95 for non-odontogenic cysts, and 0.76 for OPGs without cysts. Taken together, our results show that a combined object detection and image segmentation approach is feasible in emulating the human clinical diagnostic reasoning process in classifying cystic lesions of the jaw.

Tissue Adhesives in Reconstructive and Aesthetic Surgery-Application of Silk Fibroin-Based Biomaterials.

Tissue adhesives have been successfully used in various kind of surgeries such as oral and maxillofacial surgery for some time. They serve as a substitute for suturing of tissues and shorten treatment time. Besides synthetic-based adhesives, a number of biological-based formulations are finding their way into research and clinical application. In natural adhesives, proteins play a crucial role, mediating adhesion and cohesion at the same time. Silk fibroin, as a natural biomaterial, represents an interesting alternative to conventional medical adhesives. Here, the most commonly used bioadhesives as well as the potential of silk fibroin as natural adhesives will be discussed.

Patients' Perspectives on Artificial Intelligence in Dentistry: A Controlled Study.

Background: As artificial intelligence (AI) becomes increasingly important in modern dentistry, we aimed to assess patients' perspectives on AI in dentistry specifically for radiographic caries detection and the impact of AI-based diagnosis on patients' trust. Methods: Validated questionnaires with Likert-scale batteries (1: "strongly disagree" to 5: "strongly agree") were used to query participants' experiences with dental radiographs and their knowledge/attitudes towards AI as well as to assess how AI-based communication of a diagnosis impacted their trust, belief, and understanding. Analyses of variance and ordinal logistic regression (OLR) were used (p < 0.05). Results: Patients were convinced that "AI is useful" (mean Likert ± standard deviation 4.2 ± 0.8) and did not fear AI in general (2.2 ± 1.0) nor in dentistry (1.6 ± 0.8). Age, education, and employment status were significantly associated with patients' attitudes towards AI for dental diagnostics. When shown a radiograph with a caries lesion highlighted by an arrow, patients recognized the lesion significantly less often than when using AI-generated coloured overlays highlighting the lesion (p < 0.0005). AI-based communication did not significantly affect patients' trust in dentists' diagnosis (p = 0.44; OLR). Conclusions: Patients showed a positive attitude towards AI in dentistry. AI-supported diagnostics may assist communicating radiographic findings by increasing patients' ability to recognize caries lesions on dental radiographs.

Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

OBJECTIVE: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. MATERIALS AND METHODS: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. RESULTS: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. CONCLUSION: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.

Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine.

OBJECTIVES: Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patients. Therefore, we aimed to develop and evaluate deep learning models for the identification of clinically relevant abnormalities in bedside CXRs, using reference standards established by computed tomography (CT) and multiple radiologists. MATERIALS AND METHODS: In this retrospective study, a dataset consisting of 18,361 bedside CXRs of patients treated at a level 1 medical center between January 2009 and March 2019 was used. All included CXRs occurred within 24 hours before or after a chest CT. A deep learning algorithm was developed to identify 8 findings on bedside CXRs (cardiac congestion, pleural effusion, air-space opacification, pneumothorax, central venous catheter, thoracic drain, gastric tube, and tracheal tube/cannula). For the training dataset, 17,275 combined labels were extracted from the CXR and CT reports by a deep learning natural language processing (NLP) tool. In case of a disagreement between CXR and CT, human-in-the-loop annotations were used. The test dataset consisted of 583 images, evaluated by 4 radiologists. Performance was assessed by area under the receiver operating characteristic curve analysis, sensitivity, specificity, and positive predictive value. RESULTS: Areas under the receiver operating characteristic curve for cardiac congestion, pleural effusion, air-space opacification, pneumothorax, central venous catheter, thoracic drain, gastric tube, and tracheal tube/cannula were 0.90 (95% confidence interval [CI], 0.87-0.93; 3 radiologists on the receiver operating characteristic [ROC] curve), 0.95 (95% CI, 0.93-0.96; 3 radiologists on the ROC curve), 0.85 (95% CI, 0.82-0.89; 1 radiologist on the ROC curve), 0.92 (95% CI, 0.89-0.95; 1 radiologist on the ROC curve), 0.99 (95% CI, 0.98-0.99), 0.99 (95% CI, 0.98-0.99), 0.98 (95% CI, 0.97-0.99), and 0.99 (95% CI, 0.98-1.00), respectively. CONCLUSIONS: A deep learning model used specifically for bedside CXRs showed similar performance to expert radiologists. It could therefore be used to detect clinically relevant findings during after-hours and help emergency and intensive care physicians to focus on patient care.

Generalizability of deep learning models for dental image analysis.

We assessed the generalizability of deep learning models and how to improve it. Our exemplary use-case was the detection of apical lesions on panoramic radiographs. We employed two datasets of panoramic radiographs from two centers, one in Germany (Charité, Berlin, n = 650) and one in India (KGMU, Lucknow, n = 650): First, U-Net type models were trained on images from Charité (n = 500) and assessed on test sets from Charité and KGMU (each n = 150). Second, the relevance of image characteristics was explored using pixel-value transformations, aligning the image characteristics in the datasets. Third, cross-center training effects on generalizability were evaluated by stepwise replacing Charite with KGMU images. Last, we assessed the impact of the dental status (presence of root-canal fillings or restorations). Models trained only on Charité images showed a (mean ± SD) F1-score of 54.1 ± 0.8% on Charité and 32.7 ± 0.8% on KGMU data (p < 0.001/t-test). Alignment of image data characteristics between the centers did not improve generalizability. However, by gradually increasing the fraction of KGMU images in the training set (from 0 to 100%) the F1-score on KGMU images improved (46.1 ± 0.9%) at a moderate decrease on Charité images (50.9 ± 0.9%, p < 0.01). Model performance was good on KGMU images showing root-canal fillings and/or restorations, but much lower on KGMU images without root-canal fillings and/or restorations. Our deep learning models were not generalizable across centers. Cross-center training improved generalizability. Noteworthy, the dental status, but not image characteristics were relevant. Understanding the reasons behind limits in generalizability helps to mitigate generalizability problems.

Artificial intelligence in dental research: Checklist for authors, reviewers, readers.

OBJECTIVES: The number of studies employing artificial intelligence (AI), specifically machine and deep learning, is growing fast. The majority of studies suffer from limitations in planning, conduct and reporting, resulting in low robustness, reproducibility and applicability. We here present a consented checklist on planning, conducting and reporting of AI studies for authors, reviewers and readers in dental research. METHODS: Lending from existing reviews, standards and other guidance documents, an initial draft of the checklist and an explanatory document were derived and discussed among the members of IADR's e-oral network and the ITU/WHO focus group "Artificial Intelligence for Health (AI4H)". The checklist was consented by 27 group members via an e-Delphi process. RESULTS: Thirty-one items on planning, conducting and reporting of AI studies were agreed on. These involve items on the studies' wider goal, focus, design and specific aims, data sampling and reporting, sample estimation, reference test construction, model parameters, training and evaluation, uncertainty and explainability, performance metrics and data partitions. CONCLUSION: Authors, reviewers and readers should consider this checklist when planning, conducting, reporting and evaluating studies on AI in dentistry. CLINICAL SIGNIFICANCE: Current studies on AI in dentistry show considerable weaknesses, hampering their replication and application. This checklist may help to overcome this issue and advance AI research as well as facilitate a debate on standards in this fields.

Diagnostic performance of dynamic volume perfusion CT for differentiation of head and neck cancer from healthy tissue and post-therapeutic changes.

BACKGROUND: Post-therapeutic tissue is bradytrophic and thus has low perfusion values in PCT. In contrast, malignant tissue is expected to show higher perfusion values as cancer growth partially depends on angiogenesis. OBJECTIVES: This prospective study investigates perfusion computed tomography (PCT) for the post-therapeutic detection of cancer in the head and neck region. METHODS: 85 patients underwent PCT for 1) initial work-up of head and neck cancer (HNC; n=22) or 2) for follow-up (n=63). Regions of interest (ROIs) were placed in confirmed tumour, a corresponding location of benign tissue, and reference tissue. Perfusion was calculated using a single input maximum slope algorithm. Statistical analysis was performed with the Mann-Whitney U-test. RESULTS: PCT allowed significant differentiation of malignant tissue from post-therapeutic tissue after treatment for HNC (p=0.018). Significance was even greater after normalization of perfusion values (p=0.007). PCT allowed highly significant differentiation of HNC from reference tissue (p<0.001). CONCLUSIONS: PCT provides significantly distinct perfusion values for malignant and benign as well as post-therapeutically altered tissue in the head and neck area, thus allowing differentiation of cancer from healthy tissue. Our results show that PCT in conjunction with a standard algorithm is a potentially powerful HNC diagnostic tool.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

MOTIVATION: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results. RESULTS: Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability and implementationWe make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs.

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69(± 0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51(± 0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60(± 0.04), and an F1 score of 0.58(± 0.04) corresponding to a PPV of 0.67(± 0.05) and TPR of 0.51(± 0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.

Detecting caries lesions of different radiographic extension on bitewings using deep learning.

OBJECTIVES: We aimed to apply deep learning to detect caries lesions of different radiographic extension on bitewings, hypothesizing it to be significantly more accurate than individual dentists. METHODS: 3686 bitewing radiographs were assessed by four experienced dentists. Caries lesions were marked in a pixelwise fashion. The union of all pixels was defined as reference test. The data was divided into a training (3293), validation (252) and test dataset (141). We applied a convolutional neural network (U-Net) and used the Intersection-over-Union as validation metric. The performance of the trained neural network on the test dataset was compared against that of seven independent using tooth-level accuracy metrics. Stratification according to lesion depth (enamel lesions E1/2, dentin lesions into middle or inner third D2/3) was applied. RESULTS: The neural network showed an accuracy of 0.80; dentists' mean accuracy was significantly lower at 0.71 (min-max: 0.61-0.78, p < 0.05). The neural network was significantly more sensitive than dentists (0.75 versus 0.36 (0.19-0.65; p = 0.006), while its specificity was not significantly lower (0.83) than those of the dentists (0.91 (0.69-0.98; p > 0.05); p > 0.05). The neural network showed robust sensitivities at or above 0.70 for both initial and advanced lesions. Dentists largely showed low sensitivities for initial lesions (all except one dentist showed sensitivities below 0.25), while those for advanced ones were between 0.40 and 0.75. CONCLUSIONS: To detect caries lesions on bitewing radiographs, a deep neural network was significantly more accurate than dentists. CLINICAL SIGNIFICANCE: Deep learning may assist dentists to detect especially initial caries lesions on bitewings. The impact of using such models on decision-making should be explored.

Association of Online Learning Behavior and Learning Outcomes for Medical Students: Large-Scale Usage Data Analysis.

BACKGROUND: Digital learning environments have become very common in the training of medical professionals, and students often use such platforms for exam preparation. Multiple choice questions (MCQs) are a common format in medical exams and are used by students to prepare for said exams. OBJECTIVE: We aimed to examine whether particular learning activities contributed more strongly than others to users' exam performance. METHODS: We analyzed data from users of an online platform that provides learning materials for medical students in preparation for their final exams. We analyzed whether the number of learning cards viewed and the number of MCQs taken were positively related to learning outcomes. We also examined whether viewing learning cards or answering MCQs was more effective. Finally, we tested whether taking individual notes predicted learning outcomes, and whether taking notes had an effect after controlling for the effects of learning cards and MCQs. Our analyses from the online platform Amboss are based on user activity data, which supplied the number of learning cards studied and test questions answered. We also included the number of notes from each of those 23,633 users who had studied at least 200 learning cards and had answered at least 1000 test exam questions in the 180 days before their state exam. The activity data for this analysis was collected retrospectively, using Amboss archival usage data from April 2014 to April 2017. Learning outcomes were measured using the final state exam scores that were calculated by using the answers voluntarily entered by the participants. RESULTS: We found correlations between the number of cards studied (r=.22; P<.001) and the number of test questions that had been answered (r=.23; P<.001) with the percentage of correct answers in the learners' medical exams. The number of test questions answered still yielded a significant effect, even after controlling for the number of learning cards studied using a hierarchical regression analysis (ß=.14; P<.001; ΔR2=.017; P<.001). We found a negative interaction between the number of learning cards and MCQs, indicating that users with high scores for learning cards and MCQs had the highest exam scores. Those 8040 participants who had taken at least one note had a higher percentage of correct answers (80.94%; SD=7.44) than those who had not taken any notes (78.73%; SD=7.80; t23631=20.95; P<.001). In a stepwise regression, the number of notes the participants had taken predicted the percentage of correct answers over and above the effect of the number of learning cards studied and of the number of test questions entered in step one (ß=.06; P<.001; ΔR2=.004; P<.001). CONCLUSIONS: These results show that online learning platforms are particularly helpful whenever learners engage in active elaboration in learning material, such as by answering MCQs or taking notes.

Osteonecrosis of the jaw in patients treated with denosumab: A multicenter case series.

PURPOSE: Osteonecrosis of the jaw has been recently reported in patients receiving denosumab for the treatment of metastatic bone disease and osteoporosis. It is essential to investigate this disease as a new osteonecrosis entity in order to recognize its optimal management strategies. MATERIALS AND METHODS: A total of 63 cases of denosumab-related osteonecrosis of the jaw (DRONJ) diagnosed at two clinical centres were retrospectively reviewed. Demographics, comorbidities, antiresorptive medication use, local preceding event, location, DRONJ stage, treatment and treatment outcomes were analyzed. RESULTS: In all, 69 MRONJ lesions in 63 patients were diagnosed. The mean patient age was 70 ± 9 years. Denosumab was the only received antiresorptive medication in 50.8% of the patients. Discontinuation of denosumab prior to treatment was recorded for 66.7% of the patients, with a mean period of 6 ± 3.4 months. Stage 2 was the most common stage of the disease (71%). The lesions were predominantly located in the mandible (63.5%). The most common preceding local event was extraction (55.6%). Surgical treatment was performed in 95.7% of the cases, while purely conservative treatment was performed in 4.3%. DRONJ healed after surgical treatment in 71.7% of the treated lesions. Complete mucosal healing was achieved in 77.2% of the lesions treated with fluorescence-guided surgery (17/22). Clinical characteristics and treatment outcomes were not significantly different between patients with and without previous intake of bisphosphonates. CONCLUSION: DRONJ is more prevalent at extraction and local infection sites in cancer patients. Within the limitation of this study, surgical treatment, particularly fluorescence-guided surgery, appears to be effective for the management of DRONJ. The prior use of bisphosphonates does not seem to affect severity nor the treatment success rate of DRONJ.

Chirurgie bei Läsionen des Nervus facialis.

There are numerous reasons for facial palsy, which range from idiopathic palsy (Bell's palsy) to destruction of the facial nerve by a malignant salivary gland tumor. If the chance of spontaneous recovery is low or there is no drug therapy available, surgery is a therapeutical option. Recently, larger studies were published by specialized centers which enable a more individualized therapeutical concept to achieve tone, symmetry and movement of the paralyzed face based on a detailed preoperative assessment. An important therapy target is the improvement of patient´s quality of life. In the present article, we systematically review the important diagnostic steps and, directly derived from this, the indications for surgical options for reanimation of the mimic function. Furthermore, we provide an overview about a variety of postoperative adjuvant measures as well as on new objective assessment tools to evaluate the therapy results.

Reciprocal Predictive Accuracy of Sinonasal Symptom Severity, Nasal Endoscopy, and Frequency of Past Chronic Rhinosinusitis Exacerbations.

Objective We sought to determine whether chronic rhinosinusitis (CRS) symptom severity, endoscopic exam findings, and acute exacerbation of CRS (AECRS) frequency-all important and distinct clinical manifestations of CRS-would be predictive of each other and, therefore, inform when further assessment of each other metric should be pursued. Study Design Cross-sectional cohort study. Setting Tertiary academic rhinology clinic. Subjects and Methods In total, 241 patients with CRS were prospectively recruited and completed the 22-item Sinonasal Outcome Test (SNOT-22) to reflect CRS symptom severity. AECRS frequency was assessed using the number of sinus infections as well as CRS-related antibiotics and CRS-related oral corticosteroids used in the past 3 months. An endoscopy score was calculated for each patient. Results SNOT-22 score and AECRS were predictive of each other while AECRS and endoscopy score were not predictive of each other. SNOT-22 score could be used to predict having had, in the past 3 months, at least 1 sinus infection (area under the curve [AUC] = 0.727; P < .001), at least 1 CRS-related antibiotic used (AUC = 0.691; P < .001), or at least 1 CRS-related oral corticosteroid course used (AUC = 0.655; P < .001). Having a SNOT-22 score ≥30 could be predicted by reporting at least 1 sinus infection (AUC = 0.634; P < .001), CRS-related antibiotics (AUC = 0.614; P < .001), or CRS-related oral corticosteroids (AUC = 0.616; P < .001) in the past 3 months. These relationships held for patients with and without nasal polyps. Conclusion The predictive power of CRS outcome measures reflecting symptomatology, AECRS frequency, and endoscopic findings may be of clinical utility in situations where time or resources are limited to perform an ideally full assessment of patients with CRS.

Impact of odontogenic chronic rhinosinusitis on general health-related quality of life.

Chronic rhinosinusitis (CRS) may arise due to odontogenic etiologies. However, it is unknown whether odontogenic CRS has a differential impact on patients' quality of life (QOL) compared to standard, inflammatory (but non-odontogenic) CRS. The objective of this study was to determine whether there is a difference in the impact of sinonasal symptomatology on general health-related QOL in odontogenic CRS compared to non-odontogenic CRS. This was a retrospective review of 21 odontogenic CRS patients who visited our tertiary care center. The severity of sinonasal symptomatology and CRS-specific QOL detriment was measured using the 22-item Sinonasal Outcomes Test (SNOT-22) and general health-related QOL was measured using the health utility index from the 5-item EuroQol survey (EQ-5D HUV). Compared to non-odontogenic CRS, odontogenic CRS was not associated with a difference in SNOT-22 score [linear regression coefficient (ß) = - 1.57, 95% CI - 12.47 to 9.32, p = 0.777] but was significantly associated with decreased EQ-5D HUV (ß = - 0.10, 95% CI - 0.17 to - 0.03, p = 0.008). We also found that the magnitude of association (ß) between SNOT-22 and EQ5D-HUV was greater for odontogenic CRS patients compared to non-odontogenic CRS patients (p = 0.045). Our findings suggest sinonasal symptoms may have a greater impact on general QOL in odontogenic CRS compared to non-odontogenic CRS. The reason for this remains unknown, but deserves further study.