Radiomics in bone pathology of the jaws

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

BACKGROUND

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data.

OBJECTIVES

This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge.

METHODS

Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field.

RESULTS

AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics.

DISCUSSION

Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care.

CONCLUSIONS

Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application

OBJECTIVES

To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.

METHODS

825 condyles of 490 CBCT images from 3 centers of Stomatology hospital affliated to Zhejiang University School of Medicine were collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed.

RESULTS

The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755 mm (cortex), 0.826 mm (marrow), and 0.760 mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06 s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved.

CONCLUSIONS

The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.

The efficacy of CBCT-based radiomics techniques in differentiating between conventional and unicystic ameloblastoma

OBJECTIVE

The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB).

METHODS

In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).

RESULTS

The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance.

CONCLUSIONS

The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.

Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions

This narrative review explores texture analysis as a valuable technique in dentomaxillofacial diagnosis, providing an advanced method for quantification and characterization of different image modalities. The traditional imaging techniques rely primarily on visual assessment, which may overlook subtle variations in tissue structure. In contrast, texture analysis uses sophisticated algorithms to extract quantitative information from imaging data, thus offering deeper insights into the spatial distribution and relationships of pixel intensities. This process identifies unique "texture signatures", serving as markers for accurately characterizing tissue changes or pathological processes. The synergy between texture analysis and radiomics allows radiologists to transcend traditional size-based or semantic descriptors, offering a comprehensive understanding of imaging data. This method enhances diagnostic accuracy, particularly for the assessment of oral and maxillofacial pathologies. The integration of texture analysis with radiomics expands the potential for precise tissue characterization by moving beyond the limitations of human eye evaluations. This article reviews the current trends and methodologies in texture analysis within the field of dentomaxillofacial imaging, highlights its practical applications, and discusses future directions for research and dental clinical practice.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

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.

Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach

Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach's accuracy of 89.05%. Despite these promising results, this study's limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data.

Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma

OBJECTIVES

Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning.

METHODS

A retrospective analysis of 159 LGSCC patients with pretreatment 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed.

RESULTS

From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83.

CONCLUSION

Our findings highlighted the potential of 18 F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18 F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis

INTRODUCTION

The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications.

MATERIALS AND METHODS

A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA).

RESULTS

A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort.

CONCLUSION

The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.

CBCT for Diagnostics, Treatment Planning and Monitoring of Sinus Floor Elevation Procedures

Sinus floor elevation (SFE) is a standard surgical technique used to compensate for alveolar bone resorption in the posterior maxilla. Such a surgical procedure requires radiographic imaging pre- and postoperatively for diagnosis, treatment planning, and outcome assessment. Cone beam computed tomography (CBCT) has become a well-established imaging modality in the dentomaxillofacial region. The following narrative review is aimed to provide clinicians with an overview of the role of three-dimensional (3D) CBCT imaging for diagnostics, treatment planning, and postoperative monitoring of SFE procedures. CBCT imaging prior to SFE provides surgeons with a more detailed view of the surgical site, allows for the detection of potential pathologies three-dimensionally, and helps to virtually plan the procedure more precisely while reducing patient morbidity. In addition, it serves as a useful follow-up tool for assessing sinus and bone graft changes. Meanwhile, using CBCT imaging has to be standardized and justified based on the recognized diagnostic imaging guidelines, taking into account both the technical and clinical considerations. Future studies are recommended to incorporate artificial intelligence-based solutions for automating and standardizing the diagnostic and decision-making process in the context of SFE procedures to further improve the standards of patient care.