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Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny. [ABSTRACT FROM AUTHOR]

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Panoramic radiographs (PRs), common in annual dental evaluations, have been explored for osteoporosis detection using deep learning, but methodological flaws have cast doubt on otherwise optimistic results. This study aims to develop a robust artificial intelligence (AI) application for accurate osteoporosis identification in PRs, contributing to early and reliable diagnostics. A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. A pretrained convolutional neural network (CNN) classifier was used for training, testing, and validation with a random split of the dataset into subsets (A vs. B, A vs. C). Detection accuracy and area under the curve (AUC) were calculated. The method achieved an F1 score of 0.74 and an AUC of 0.8401 (A vs. B). For young patients (A vs. C), it performed with 98% accuracy and an AUC of 0.9812. This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. It also highlights the importance of methodological rigor, as not all optimistic results are credible. [ABSTRACT FROM AUTHOR]

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.

Objectives: Facial nerve schwannomas (FNSs) and vestibular nerve schwannomas (VNSs) may occupy similar locations in the internal auditory canal (IAC). Risk factors for hearing loss in patients with FNS are unknown. Similar to VNSs, we hypothesize that the size and location of FNSs do not correlate with hearing loss.Study Design: Retrospective cohort study and otopathology case review.Methods: A single institution retrospective review of all patients diagnosed with FNS was performed. Demographic data, tumor size and location, severity and type of hearing loss, and facial nerve function were analyzed. Otopathologic review of a case of FNS was also performed.Results: Thirty-three patients with FNS were identified. Mean age was 44 years, and 61% (n = 21) were female. Nine patients had tumors involving the IAC facial nerve, whereas 24 had distal tumors without IAC involvement. Eleven patients (33%) had hearing loss, and 55% of patients with sensorineural hearing loss (SNHL) had tympanic segment tumors without IAC involvement. The presence of SNHL was not associated with IAC tumor location (P = 0.21). Maximal dimensions of FNSs were 3 mm to 42 mm, without correlation between size and SNHL severity (r = -0.017, P = 0.926). Otopathologic review of an untreated 5-mm IAC FNS demonstrated disproportionate spiral ganglion and hair cell loss within the ipsilateral cochlea compared to the contralateral ear.Conclusion: Tympanic segment FNS without IAC involvement may result in SNHL. Tumor size is not associated with the presence or severity of SNHL. Otopathologic review suggests degenerative cochlear changes in FNS. Mechanisms for SNHL in schwannomas are likely multifactorial and may be unrelated to cochlear nerve compression.Level Of Evidence: 4. Laryngoscope, 127:1676-1682, 2017. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to determine whether differences in long-term facial function outcomes following acute Lyme disease-associated facial palsy (LDFP) exist between patients who received antibiotic monotherapy (MT); dual therapy (DT) with antibiotics and corticosteroids; and triple therapy (TT) with antibiotics, corticosteroids, and antivirals.Study Design: Retrospective cohort.Methods: All patients with a prior diagnosis of unilateral LDFP who presented to our center between 2002 and 2015 were retrospectively assessed for inclusion. Two blinded experts graded static, dynamic, and synkinesis parameters of facial functions using standardized video documentation of facial function.Results: Fifty-one patients were included. The mean time of assessment following LDFP onset was 15.1 months (range 0.3-84 months). Significantly worse facial outcomes were seen among those who received DT and TT as compared to those who received MT, most pronounced among those assessed 12 months or later following onset of LDFP (Dynamic-P = 0.031, post hoc MT vs. TT: mean difference [MD], 15.83; 95% confidence interval [CI], 1.54-30.13; P = 0.030. Synkinesis-P = 0.026, post hoc MT vs. DT: MD, 21.50; 95% CI, 0.68-42.32; P = 0.043, post hoc MT vs. TT: MD, 19.22; 95% CI, 2.23-36.22; P = 0.027).Conclusion: An association between corticosteroid use in acute LDFP and worse long-term facial function outcomes has been demonstrated. Care should be taken in differentiating viral or idiopathic facial palsy (e.g., Bell palsy) from LDFP.Level Of Evidence: 4. Laryngoscope, 127:1451-1458, 2017. [ABSTRACT FROM AUTHOR]

We report a case of a 63 year old woman with progressive small red spots that can not be diminished with pressure. They appeared on the feet first and spread out to the legs, trunk and arms. This was accompanied with non-bloody diarrhoea and the laboratory diagnostics showed minor impairment of the kidney. The biopsy of the skin showed deposits of immunoglobulin A and C3 complement in the stratum papillare of the small vessels and a necrosis of the wall, which made the diagnosis of the Schönlein-Henoch purpura clear. It was accompanied by a systemic participation of the gastrointestinal tract. The quick reversible renal insufficiency was rather interpreted as a prerenal cause, than as an origin of the Schönlein-Henoch purpura. We further discussed the diagnostic methods, the aetiology and the possible therapy options. [ABSTRACT FROM AUTHOR]