A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts

Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

Objective: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). Methods: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. Results: The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. Conclusions: The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis

Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

This study’s objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.7 y) experiencing signs and symptoms of the disease for less than 10 y and 17 age- and sex-matched control subjects (39.4 ± 15.2 y) completed a questionnaire, had a temporomandibular joint clinical exam, had blood and saliva samples drawn, and had high-resolution cone beam computed tomography scans taken. Serum and salivary levels of 17 inflammatory biomarkers were quantified using protein microarrays. A NN was trained with 259 other condyles to detect and classify the stage of TMJOA and then compared to repeated clinical experts’ classifications. Levels of the salivary biomarkers MMP-3, VE-cadherin, 6Ckine, and PAI-1 were correlated to each other in TMJOA patients and were significantly correlated with condylar morphological variability on the posterior surface of the condyle. In serum, VE-cadherin and VEGF were correlated with one another and with significant morphological variability on the anterior surface of the condyle, while MMP-3 and CXCL16 presented statistically significant associations with variability on the anterior surface, lateral pole, and superior-posterior surface of the condyle. The range of mouth opening variables were the clinical markers with the most significant associations with morphological variability at the medial and lateral condylar poles. The repeated clinician consensus classification had 97.8% agreement on degree of degeneration within 1 group difference. Predictive analytics of the NN’s staging of TMJOA compared to the repeated clinicians’ consensus revealed 73.5% and 91.2% accuracy. This study demonstrated significant correlations among variations in protein expression levels, clinical symptoms, and condylar surface morphology. The results suggest that 3-dimensional variability in TMJOA condylar morphology can be comprehensively phenotyped by the NN

Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository

Avaliação da importância da coloração de Perls na rotina de mielogramas de pacientes com anemia associada a uma ou mais citopenias em sangue periférico Evaluation of the importance of Perls stain in the routine testing of myelograms of patients with anemia associated with one or more peripheral blood cytopenias

As síndromes mielodisplásicas (SMD) são um grupo heterogêneo de doenças malignas das células-tronco hematopoéticas, classificadas segundo a Organização Mundial da Saúde (OMS) em: anemia refratária, anemia refratária com sideroblastos em anel, citopenia refratária com displasia de multilinhagens, anemia refratária com excesso de blastos, síndrome mielodisplásica inclassificável e sindrome mielodisplásica associada com anormalidade isolada do cromossomo 5q(del). Na anemia refratária com sideroblastos em anel observam-se hiperplasia e displasia eritróide com presença de 15% ou mais de sideroblastos em anel. Utilizamos neste estudo a coloração de Perls em esfregaços de medula óssea de pacientes com idade superior a 40 anos e que apresentavam uma ou mais citopenias no sangue periférico associada a anemia. Por tratar-se de técnica de manejo fácil e ágil sugerimos seu emprego em esfregaços de aspirado de medula óssea de pacientes que apresentem os achados laboratoriais acima, pois, dentre os casos analisados 18,7% apresentavam mais que 10 grânulos sideróticos circundando a terça parte ou mais do núcleo do precursor eritróide (sideroblasto em anel), sugerindo ao hematologista um possível diagnóstico de Síndrome Mielodisplásica com Sideroblastos em Anel (SMD-ARSA). Importante relatar que a grande maioria destes casos com aumento de sideroblastos em anel não foi encaminhada ao nosso serviço, com suspeita de SMD, e em somente um caso foi solicitada a realização da coloração de Perls.Myelodisplastic syndromes are a heterogeneous group of malignant haematopoietic stem cells. They are classified by the World Health Organization as refractory anemia, refractory anemia with ringed sideroblasts, refractory cytopenia with multilineage displasia, refractory anemia with excess of blast cells, unclassified myelodisplastic syndrome and myelodisplastic syndrome associated with a 5q chromosomal delection. Refractory anemia with ringed sideroblasts is defined by red blood cell hyperplasia and dysplasia with 15% or more of ringed sideroblasts. We studied bone marrow aspirates using Perls' stain with blood smears from over 40-year-old patients that had one or more cytopenias in their peripheral blood associated with anemia. A total of 18.7% of patients had ringed sideroblasts leading to a possible diagnosis of refractory anemia with ringed sideroblasts, one of the myelodisplastic syndromes. Most of those cases were refered to our service without clinical suspicion of myelodisplastic syndrome and in only one case Perls' stain was requested. Perls' stain is easily performed and the results are fast and so we suggest that it should be routinely used in all cases of possible myelodisplastic syndrome