Inter and intra-operator reliability of Lekholm and Zarb classification and proposal of a novel radiomic data-driven clustering for qualitative assessment of edentulous alveolar ridges.

OBJECTIVES: The present study was conducted to evaluate the reproducibility of Lekholm and Zarb classification system (L&Z) for bone quality assessment of edentulous alveolar ridges and to investigate the potential of a data-driven approach for bone quality classification. MATERIALS AND METHODS: Twenty-six expert clinicians were asked to classify 110 CBCT cross-sections according to L&Z classification (T0). The same evaluation was repeated after one month with the images put in a different order (T1). Intra- and inter-examiner agreement analyses were performed using Cohen's kappa coefficient (CK) and Fleiss' kappa coefficient (FK), respectively. Additionally, radiomic features extraction was performed from 3D edentulous ridge blocks derived from the same 110 CBCTs, and unsupervised clustering using 3 different clustering methods was used to identify patterns in the obtained data. RESULTS: Intra-examiner agreement between T0 and T1 was weak (CK 0.515). Inter-examiner agreement at both time points was minimal (FK at T0: 0.273; FK at T1: 0.243). The three different unsupervised clustering methods based on radiomic features aggregated the 110 CBCTs in three groups in the same way. CONCLUSIONS: The results showed low agreement among clinicians when using L&Z classification, indicating that the system may not be as reliable as previously thought. The present study suggests the possible application of a reproducible data-driven approach based on radiomics for the classification of edentulous alveolar ridges, with potential implications for improving clinical outcomes. Further research is needed to determine the clinical significance of these findings and to develop more standardized and accurate methods for assessing bone quality of edentulous alveolar ridges.

Immediate loading full-arch rehabilitation using transmucosal tissue-level implants with different variables associated: a one-year observational study.

BACKGROUND: The aim of the present observational study was to investigate the application of transmucosal tissue-level implants in immediate loading full-arch rehabilitation with different variables associated. METHODS: Patients needing a full-arch implant rehabilitation were recruited and rehabilitated with four transmucosal tissue level implants. Data related to implants' diameters and lengths, jaw distributions, and presence of angulated abutments were collected. The following outcomes were evaluated: survival rate, marginal bone loss (MBL), Plaque Index (PI), bleeding on probing (BoP), probing depth (PD). Descriptive statistical analysis was reported and univariate linear regression models were built to assess a significant correlation between MBL and the different implant related factors. RESULTS: Twenty patients were rehabilitated for a total implant number of 80; 11 rehabilitations were performed on the maxilla, while 9 were performed on the mandible; 48 implants presented a 3.8 mm diameter and 32 implants presented a 4.25 mm diameter. Implants length varied between 10 to 15 mm; 40 tilted implants were connected to angulated abutment, while 40 straight implants were connected directly to the prostheses (no abutments). At the one year follow-up visit no implants failed resulting in an implant survival rate of 100%. The overall MBL was 1.19±0.30 mm. No statistically significant difference (P>0.05) was highlighted among any of the subgroups analyzed. CONCLUSIONS: Despite different variables associated, tissue level implants seem to represent a valid option when applied in immediate loading full-arch rehabilitation. Further research and longer observational periods are encouraged to confirm the result.

Can radiomic features extracted from intra-oral radiographs predict physiological bone remodelling around dental implants? A hypothesis-generating study.

AIM: The rate of physiological bone remodelling (PBR) occurring after implant placement has been associated with the later onset of progressive bone loss and peri-implantitis, leading to medium- and long-term implant therapy failure. It is still questionable, however, whether PBR is associated with specific bone characteristics. The aim of this study was to assess whether radiomic analysis could reveal not readily appreciable bone features useful for the prediction of PBR. MATERIALS AND METHODS: Radiomic features were extracted from the radiographs taken at implant placement (T0) using LifeX software. Because of the multi-centre design of the source study, ComBat harmonization was applied to the cohort. Different machine-learning models were trained on selected radiomic features to develop and internally validate algorithms capable of predicting high PBR. In addition, results of the algorithm were included in a multivariate analysis with other clinical variables (tissue thickness and depth of implant position) to test their independent correlation with PBR. RESULTS: Specific radiomic features extracted at T0 are associated with higher PBR around tissue-level implants after 3 months of unsubmerged healing (T1). In addition, taking advantage of machine-learning methods, a naive Bayes model was trained using radiomic features selected by fast correlation-based filter (FCBF), which showed the best performance in the prediction of PBR (AUC = 0.751, sensitivity = 66.0%, specificity = 68.4%, positive predictive value = 73.3%, negative predictive value = 60.5%). In addition, results of the whole model were included in a multivariate analysis with tissue thickness and depth of implant position, which were still found to be independently associated with PBR (p-value < .01). CONCLUSION: The combination of radiomics and machine-learning methods seems to be a promising approach for the early prediction of PBR. Such an innovative approach could be also used for the study of not readily disclosed bone characteristics, thus helping to explain not fully understood clinical phenomena. Although promising, the performance of the radiomic model should be improved in terms of specificity and sensitivity by further studies in this field.

Advanced Cutaneous Leiomyosarcoma of the Forearm.

Leiomyosarcoma is a malignant smooth muscle neoplasm, which is traditionally divided into superficial and deep tumors. Superficial leiomyosarcomas are quite rare entities, accounting for approximately 7% of soft tissue neoplasms and 0.04% of all cancers. Here we describe a rare case of advanced primary cutaneous leiomyosarcoma (PCL) in a 93-year-old woman, highlighting the considerable size of the lesion and the correct surgical and oncological management. The clinical story began about 4 years ago, and the neoplasia was treated only with local radiotherapy, but the patient suffered from a dramatic volumetric increase of the right arm sarcoma one year ago. Then, an amputation of the limb was performed without following adjuvant chemotherapy. Currently, she does not show signs of recurrence and is in good shape.

Underwater Robotics Competitions: The European Robotics League Emergency Robots Experience With FeelHippo AUV.

Underwater robots are nowadays employed for many different applications; during the last decades, a wide variety of robotic vehicles have been developed by both companies and research institutes, different in shape, size, navigation system, and payload. While the market needs to constitute the real benchmark for commercial vehicles, novel approaches developed during research projects represent the standard for academia and research bodies. An interesting opportunity for the performance comparison of autonomous vehicles lies in robotics competitions, which serve as an useful testbed for state-of-the-art underwater technologies and a chance for the constructive evaluation of strengths and weaknesses of the participating platforms. In this framework, over the last few years, the Department of Industrial Engineering of the University of Florence participated in multiple robotics competitions, employing different vehicles. In particular, in September 2017 the team from the University of Florence took part in the European Robotics League Emergency Robots competition held in Piombino (Italy) using FeelHippo AUV, a compact and lightweight Autonomous Underwater Vehicle (AUV). Despite its size, FeelHippo AUV possesses a complete navigation system, able to offer good navigation accuracy, and diverse payload acquisition and analysis capabilities. This paper reports the main field results obtained by the team during the competition, with the aim of showing how it is possible to achieve satisfying performance (in terms of both navigation precision and payload data acquisition and processing) even with small-size vehicles such as FeelHippo AUV.