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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Plasma Electrolytic Oxidation of Titanium Implant Surfaces: Microgroove-Structures Improve Cellular Adhesion and Viability.

BACKGROUND/AIM: Plasma electrolytic oxidation (PEO) is an established electrochemical treatment technique that can be used for surface modifications of metal implants. In this study we to treated titanium implants with PEO, to examine the resulting microstructure and to characterize adhesion and viability of cells on the treated surfaces. Our aim was to identify an optimal surface-modification for titanium implants in order to improve soft-tissue integration. MATERIALS AND METHODS: Three surface-variants were generated on titanium alloy Ti6Al4V by PEO-treatment. The elemental composition and the microstructures of the surfaces were characterized using energy dispersive X-ray spectroscopy, scanning electron microscopy and profilometry. In vitro cytocompatibility of the surfaces was assessed by seeding L929 fibroblasts onto them and measuring the adhesion, viability and cytotoxicity of cells by means of live/dead staining, XTT assay and LDH assay. RESULTS: Electron microscopy and profilometry revealed that the PEO-surface variants differed largely in microstructure/topography, porosity and roughness from the untreated control material as well as from one another. Roughness was generally increased after PEO-treatment. In vitro, PEO-treatment led to improved cellular adhesion and viability of cells accompanied by decreased cytotoxicity. CONCLUSION: PEO-treatment provides a promising strategy to improve the integration of titanium implants with surrounding tissues.

Facial palsy after dental procedures - Is viral reactivation responsible?

OBJECTIVES: Herpes labialis viral reactivation has been reported following dental procedures, but the incidence, characteristics and outcomes of delayed peripheral facial nerve palsy following dental work is poorly understood. Herein we describe the unique features of delayed facial paresis following dental procedures. MATERIALS AND METHODS: An institutional retrospective review was performed to identify patients diagnosed with delayed facial nerve palsy within 30 days of dental manipulation. Demographics, prodromal signs and symptoms, initial medical treatment and outcomes were assessed. RESULTS: Of 2471 patients with facial palsy, 16 (0.7%) had delayed facial paresis following ipsilateral dental procedures. Average age at presentation was 44 yrs and 56% (9/16) were female. Clinical evaluation was consistent with Bell's palsy in 14 (88%) and Ramsay-Hunt syndrome in 2 patients (12%). Patients developed facial paresis an average of 3.9 days after the dental procedure, with all individuals developing a flaccid paralysis (House Brackmann (HB) grade VI) during the acute stage. 50% of patients developed persistent facial palsy in the form of non-flaccid facial paralysis (HBIII-IV). CONCLUSION: Facial palsy, like herpes labialis, can occur in the days following dental procedures and may also be related to viral reactivation. In this small cohort, long-term facial outcomes appear worse than for spontaneous Bell's palsy.

Approaches to Peripheral Nerve Repair: Generations of Biomaterial Conduits Yielding to Replacing Autologous Nerve Grafts in Craniomaxillofacial Surgery.

Peripheral nerve injury is a common clinical entity, which may arise due to traumatic, tumorous, or even iatrogenic injury in craniomaxillofacial surgery. Despite advances in biomaterials and techniques over the past several decades, reconstruction of nerve gaps remains a challenge. Autografts are the gold standard for nerve reconstruction. Using autografts, there is donor site morbidity, subsequent sensory deficit, and potential for neuroma development and infection. Moreover, the need for a second surgical site and limited availability of donor nerves remain a challenge. Thus, increasing efforts have been directed to develop artificial nerve guidance conduits (ANCs) as new methods to replace autografts in the future. Various synthetic conduit materials have been tested in vitro and in vivo, and several first- and second-generation conduits are FDA approved and available for purchase, while third-generation conduits still remain in experimental stages. This paper reviews the current treatment options, summarizes the published literature, and assesses future prospects for the repair of peripheral nerve injury in craniomaxillofacial surgery with a particular focus on facial nerve regeneration.

Microvascular stent anastomosis using N-fibroin stents: feasibility, ischemia time, and complications.

OBJECTIVE: To evaluate a novel microvascular anastomosis technique using N-fibroin stents. STUDY DESIGN: Cylinder stents of 1 mm diameter and 5 mm length were fabricated using N-fibroin from silkworms. In 22 rats, aortas were dissected, and the stent was inserted into the two ends of the aorta and fixed using methylmethacrylate. RESULTS: Stent anastomosis was successful in 21 (96%) rats. The mean ischemia time was 7.4 minutes, significantly shorter than the 15.9 minutes in the control group with conventional sutures (P < .0001). After 4 months, anastomosis was functionally patent in all cases. However, elastic fibers remained interrupted in all stent anastomosis cases, and marked host rejection was evident at the stent anastomosis sites. Around the stents, thrombi were frequent (52%). CONCLUSIONS: Our study demonstrated the basic feasibility of stent anastomosis using N-fibroin stents and reduced ischemia time. However, thrombus formation, frequent and severe abdominal infections, and heavy host rejection remain critical issues.