David Steybe

Background: Managing oral mucosal lesions in general dental practice is challenging, as visually similar conditions may require very different treatments. We developed an AI‐based triage system connecting intraoral image analysis with guideline‐based treatment recommendations, tested it externally, and compared its performance with clinicians' recommendations. Methods: YOLOv8 and YOLOv11 were trained on 4079 intraoral photographs to classify 11 oral mucosal lesion types. These were mapped into five treatment tiers defined by urgency and management aim: T1 specialist referral for malignant and high‐risk lesions, T2 interdisciplinary referral for specialist‐led non‐cancer management, T3 risk‐adapted surveillance, T4 dental treatment with reassessment, and T5 no treatment. Model performance was compared with two general dentists and four specialists on an internal test set. YOLOv11 was additionally tested on an external dataset. Results: YOLOv11 outperformed YOLOv8 in macro‐averaged precision, recall, and F1 score, achieving 0.72, 0.74, and 0.73 respectively. However, 25% of T1 high‐risk cases were under‐triaged. YOLOv11 significantly outperformed all clinicians, whose F1 scores ranged from 0.33 to 0.69. External performance decreased markedly (F1 score: 0.58), consistent with domain shift. Conclusion: Guideline‐oriented AI triage is feasible but requires multicentre training for improved generalizability, as well as safety‐focused optimization, and prospective user evaluation before clinical use.

Computer technology–based treatment approaches like intraoperative navigation and intensity-modulated radiation therapy have become important components of state of the art head and neck cancer treatment. Multidirectional exchange of virtual three-dimensional patient data via an interdisciplinary platform allows all medical specialists involved in the patients treatment to take full advantage of these technologies. This review article gives an overview of current technologies and future directions regarding treatment approaches that are based on a virtual, three-dimensional patient specific dataset: storage and exchange of spatial information acquired via intraoperative navigation allow for a highly precise frozen section procedure. In the postoperative setting, virtual reconstruction of the tumor resection surface provides the basis for improved radiation therapy planning and virtual reconstruction of the tumor with integration of molecular findings creates a valuable tool for postoperative treatment and follow-up. These refinements of established treatment components and novel approaches have the potential to make a major contribution to improving the outcome in head and neck cancer patients.

Objectives: To investigate whether in patients undergoing surgery for oral squamous cell carcinoma, stimulated Raman histology (SRH), in comparison with H&E-stained frozen sections, can provide accurate diagnoses regarding neoplastic tissue and sub-classification of non-neoplastic tissues. Materials and methods: SRH, a technology based on Raman scattering, was applied to generate digital histopathologic images of 80 tissue samples obtained from 8 oral squamous cell carcinoma (OSCC) patients. Conventional H&E-stained frozen sections were then obtained from all 80 samples. All images/sections (SRH and H&E) were analyzed for squamous cell carcinoma, normal mucosa, connective tissue, muscle tissue, adipose tissue, salivary gland tissue, lymphatic tissue, and inflammatory cells. Agreement between SRH and H&E was evaluated by calculating Cohen’s kappa. Accuracy of SRH compared to H&E was quantified by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as well as area under the receiver operating characteristic curve (AUC). Results: Thirty-six of 80 samples were classified as OSCC by H&E-based diagnosis. Regarding the differentiation between neoplastic and non-neoplastic tissue, high agreement between H&E and SRH (kappa: 0.880) and high accuracy of SRH (sensitivity: 100%; specificity: 90.91%; PPV: 90.00%, NPV: 100%; AUC: 0.954) were demonstrated. For sub-classification of non-neoplastic tissues, SRH performance was dependent on the type of tissue, with high agreement and accuracy for normal mucosa, muscle tissue, and salivary glands. Conclusion: SRH provides high accuracy in discriminating neoplastic and non-neoplastic tissues. Regarding sub-classification of non-neoplastic tissues in OSCC patients, accuracy varies depending on the type of tissue examined. Clinical relevance: This study demonstrates the potential of SRH for intraoperative imaging of fresh, unprocessed tissue specimens from OSCC patients without the need for sectioning or staining.

Background: This study evaluated the accuracy of computer‐assisted surgery (CAS)–drivenDCIA(deep circumflex iliac artery) flap mandibular reconstruction by traditional morphometric methods and geometric morphometric methods (GMM).Methods: Reconstruction accuracy was evaluated by measuring distances and angles between bilateral anatomical landmarks. Additionally, the average length of displacements vectors between landmarks was computed to evaluate factors assumed to influence reconstruction accuracy. Principal component analysis (PCA) was applied to unveil main modes of dislocation.Results: High reconstruction accuracy could be demonstrated for a sample consisting of 26 patients. The effect of the number of segments and length of defect on reconstruction accuracy were close to the commonly used significance threshold (p = 0.062/0.060). PCA demonstrated displacement to result mainly from sagittal and transversal shifts.Conclusions: CAS is a viable approach to achieve high accuracy in mandibular reconstruction and GMM can facilitate the evaluation of factors influencing reconstruction accuracy and unveil main modes of dislocation in this context.

Background and Objectives: The objective of this study was to evaluate the effects of bisphosphonate (BP) administration on tooth growth, using CT-data of a minipig animal model investigation. Materials and Methods: Tooth growth was evaluated in minipigs, with eight animals receiving weekly zoledronate (ZOL) and three animals serving as the control group. Tooth growth was evaluated at the right 2nd molar (M2) in the maxilla. A computed tomography-based measuring method was applied to evaluate tooth growth in the coronal-apical, buccal-oral and mesial-distal axis. Results: ZOL-administration was found to impact tooth growth in all evaluated measuring axes, with the highest effect observed in the coronal-apical axis. Conclusions: Detrimental effects of BP administration on growing teeth have been reported by a number of investigators. The results of this investigation demonstrate that intravenous ZOL affects the growth of the whole tooth within a short period of administration. With BPs being administered to a growing number of pediatric patients, further studies should be conducted to qualify and quantify the effects of BPs on developing teeth.

Purpose: Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans. Methods: The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD). Results: Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen. Conclusion: In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.

Adequate soft-tissue dimensions have been shown to be crucial for the long-term success of dental implants. To date, there is evidence that placement of dental implants should only be conducted in an area covered with attached gingiva. Modern implant planning software does not visualize soft-tissue dimensions. This study aims to calculate the course of the mucogingival borderline (MG-BL) using statistical shape models (SSM). Visualization of the MG-BL allows the practitioner to consider the soft tissue supply during implant planning. To deploy an SSM of the MG-BL, healthy individuals were examined and the intra-oral anatomy was captured using an intra-oral scanner (IOS). The empirical anatomical data was superimposed and analyzed by principal component analysis. Using a Leave-One-Out Cross Validation (LOOCV), the prediction of the SSM was compared with the original anatomy extracted from IOS. The median error for MG-BL reconstruction was 1.06 mm (0.49–2.15 mm) and 0.81 mm (0.38–1.54 mm) for the maxilla and mandible, respectively. While this method forgoes any technical work or additional patient examination, it represents an effective and digital method for the depiction of soft-tissue dimensions. To achieve clinical applicability, a higher number of datasets has to be implemented in the SSM.

Intraoperative incorporation of radiopaque fiducial markers at the tumor resection surface can provide useful assistance in identifying the tumor bed in postoperative imaging for RT planning and radiological follow-up. Besides titanium clips, iodine containing injectable liquid fiducial markers represent an option that has emerged more recently for this purpose. In this study, marking oral soft tissue resection surfaces, applying low dose injections of a novel Conformité Européenne (CE)-marked liquid fiducial marker based on sucrose acetoisobutyrate (SAIB) and iodinated SAIB (x-SAIB) was investigated. Visibility and discriminability of low dose injections of SAIB/x-SAIB (10 µl, 20 µl, 30 µl) were systematically studied at different kV settings used in clinical routine in an ex-vivo porcine mandible model. Transferability of the preclinical results into the clinical setting and applicability of DE-CT were investigated in initial patients. Markers created by injection volumes as low as 10 µl were visible in CT imaging at all kV settings applied in clinical routine (70–120 kV). An injection volume of 30 µl allowed differentiation from an injection volume of 10 µl. In a total of 118 injections performed in two head and neck cancer patients, markers were clearly visible in 83% and 86% of injections. DE-CT allowed for differentiation between SAIB/x-SAIB markers and other hyperdense structures. Injection of low doses of SAIB/x-SAIB was found to be a feasible approach to mark oral soft tissue resection surfaces, with injection volumes as low as 10 µl found to be visible at all kV settings applied in clinical routine. With the application of SAIB/x-SAIB reported for tumors of different organs already, mostly applying relatively large volumes for IGRT, this study adds information on the applicability of low dose injections to facilitate identification of the tumor bed in postoperative CT and on performance of the marker at different kV settings used in clinical routine.

Objective: To evaluate a novel liquid fiducial marker for intraoperative marking of the tumour resection surface in oral cancer patients to facilitate precise postoperative delineation of the interface between the tumour resection border and reconstructed tissue for intensity-modulated radiation therapy. Methods: A total of 200 markers were created by injecting the volumes of 10 µl, 20 µl, 30 µl, 40 µl and 50 µl of a liquid marker composed of sucrose acetoisobutyrate (SAIB) and iodinated sucrose acetoisobutyrate (x-SAIB) into the soft tissue of porcine mandible segments. Visibility of the resulting markers was quantified by threshold-based segmentation of the marker volume in CT- and CBCT imaging and by a comparison of signal intensities in MRI. Results: Even the lowest volume of SAIB-/x-SAIB investigated (10 µl) resulted in a higher visibility (CT_{Soft tissue}: 88.18 ± 13.23 µl; CT_Bone: 49.55 ± 7.62 µl; CBCT: 54.65 ± 12.58 µl) than observed with the incorporation of titanium ligature clips (CT_{Soft tissue}: 50.15 ± 7.50 mm³; CT_Bone: 23.90 ± 3.39 mm³; CBCT: 33.80 ± 9.20 mm³). Markers created by the injection of 10 µl and 20 µl could reliably be delineated from markers created by the injection of higher volumes. Conclusion: SAIB/x-SAIB, which has recently become available as a Conformité Européenne (CE)-marked fiducial marker, provides an option for fast and reliable production of markers with excellent visibility in imaging modalities used in oral cancer radiation therapy (RT) planning routine.

The aim of this study was to evaluate the concentration of penicillin G in bone affected by antiresorptive agent-related osteonecrosis of the jaw (ARONJ) following a single preoperative dose of 10 million international units (6000 mg). ARONJ is a major concern in patients administered antiresorptive agents for conditions associated with pathologically increased bone resorption. Antibiotic therapy is a key component of most treatment approaches for ARONJ and penicillin based regimens, providing a cost effective therapy option with a favorable side effect profile, are administered most frequently. In this study, high performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS) was applied to evaluate penicillin G concentration in serum and bone samples of 19 patients suffering from ARONJ and undergoing surgical treatment under perioperative intravenous (IV) antibiotic therapy. Penicillin G bone concentrations were above the limit of detection (0.1 μg/g bone tissue) in 16 out of 19 samples, with a median concentration of 2.7 μg/g (range 0.1–8.8 μg/g). Penicillin G concentrations in intraoperative serum samples were above the limit of detection in all serum samples, with a median concentration of 116 μg/mL (range 1–232 μg/mL). Thus, considering bacteria frequently found in ARONJ lesions, penicillin G at levels providing adequate antimicrobial activity was detected in the serum and 16 out of 19 osteonecrotic lesions of patients suffering from ARONJ.