Leena R. David

Paediatric pelvic radiography is a frequently performed diagnostic procedure that exposes radiosensitive organs to ionising radiation. Optimisation of imaging parameters is essential to minimise radiation dose while preserving diagnostic image quality. This systematic review aimed to evaluate technical strategies for radiation dose optimisation in paediatric pelvic radiography and assess their impact on radiation dose and image quality. A systematic search of PubMed, Scopus, Embase, and Web of Science was conducted for studies published between 2004 and 2025. Experimental and clinical studies assessing radiation dose and image quality in paediatric pelvic radiography were included. Data on exposure parameters, dose metrics, and image quality assessment methods were extracted and synthesised narratively. Nine studies met the inclusion criteria, predominantly comprising phantom-based experimental designs. Increasing tube potential (kVp), extending source-to-image distance (SID), and applying additional aluminium or copper filtration consistently reduced radiation dose. Dose reductions of up to 60–70% were reported without clinically significant loss of image quality. Although objective image quality metrics (e.g., CNR, SNR) occasionally declined, subjective assessments confirmed that images remained diagnostically acceptable. Optimisation of technical parameters in paediatric pelvic radiography can achieve substantial radiation dose reduction while maintaining clinically acceptable image quality. However, the predominance of phantom-based evidence highlights the need for further patient-based clinical studies to support routine implementation.

Accessory ostium [AO] is one of the important anatomical variations in the maxillary sinus. AO is often associated with sinus pathology. Radiographic imaging plays a very important role in the detection of AO. Deep learning models have been used in maxillofacial imaging for interpretation and segmentation. However, there have been no research papers investigating the effectiveness of CNN in detecting AO in radiographs. To fill this gap of knowledge, we conducted a study to determine the accuracy of deep learning models in detecting AO in coronal CBCT images. Two examiners collected 454 coronal section images (227 with AO and 227 without AO) from 856 large field of view [FOV] cone beam tomography [CBCT] scans in the dental radiology archives of a teaching hospital. The collected images were then pre-processed and augmented to obtain 1260 images. Three pre-trained models, the Visual Geometry Group of the University of Oxford-16 layers [VGG16], MobileNetV2, and ResNet101V2, were used as base models. The performance of all the models was analyzed, and ResNet101v2 was selected for classification of images. Fine-tuning approach was employed with L1 (Lasso regression) regularization to avoid overfitting. The test accuracy and loss of the ResNet-101V2 classification model was 0.81 and 0.51, respectively. The precision, recall, F1-score, and AUC of the classification model were 0.82, 0.81, 0.81, and 0.87 respectively. ResNet-101V2 showed good accuracy in the detection of AO from coronal CBCT images. The present study used cropped two-dimensional images of CBCT scans. Future work can be carried out to determine the accuracy of deep learning models in the detection of AO in three-dimensional CBCT scans.

Background: Pneumatization of turbinates, also known as concha bullosa (CB), is associated with nasal septal deviation and sinonasal pathologies. This study aims to evaluate the performance of deep learning models in detecting CB in coronal cone-beam computed tomography (CBCT) images.Methods: Standardized coronal images were obtained from 203 CBCT scans (83 with CB and 119 without CB) from the radiology archives of a dental teaching hospital. These scans underwent preprocessing through a hybridized contrast enhancement (CE) method using discrete wavelet transform (DWT). Of the 203 CBCT images, 162 were randomly assigned to the training set and 41 to the testing set. Initially, the images were enhanced using a CE technique before being input into pre-trained deep learning models, namely ResNet50, ResNet101, and MobileNet. The features extracted by each model were then flattened and input into a random forest (RF) classifier. In the subsequent phase, the CE technique was refined by incorporating DWT.Results: CE-DWT-ResNet101-RF demonstrated the highest performance, achieving an accuracy of 91.7% and an area under the curve (AUC) of 98%. In contrast, CE-MobileNet-RF recorded the lowest accuracy at 82.46% and an AUC of 92%. The highest precision, recall, and F1 score (all 92%) were observed for CE-DWT-ResNet101-RF.Conclusion: Deep learning models demonstrated high accuracy in detecting CB in CBCT images. However, to confirm these results, further studies involving larger sample sizes and various deep learning models are required.

Radiographs play a key role in diagnosis of periodontal diseases. Deep learning models have been explored for image analysis in periodontal diseases. However, there is lacuna of research in the deep learning model-based detection of furcation involvements [FI]. The objective of this study was to determine the accuracy of deep learning model in the detection of FI in axial CBCT images. We obtained initial dataset 285 axial CBCT images among which 143 were normal (without FI) and 142 were abnormal (with FI). Data augmentation technique was used to create 600(300 normal and 300 abnormal) images by using 200 images from the training dataset. Remaining 85(43 normal and 42 abnormal) images were kept for testing of model. ResNet101V2 with transfer learning was used employed for the analysis of images. Training accuracy of model is 98%, valid accuracy is 97% and test accuracy is 91%. The precision and F1 score were 0.98 and 0.98 respectively. The Area under curve (AUC) was reported at 0.98. The test loss was reported at 0.2170. The deep learning model (ResNet101V2) can accurately detect the FI in axial CBCT images. However, since our study was preliminary in nature and carried out with relatively smaller dataset, a study with larger dataset will further confirm the accuracy of deep learning models.

Background: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. Objective: This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. Methods: Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones—ResNet50, ResNet101, and MobileNet—were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. Results: The best-performing model—CEH-ResNet101—achieved a mean average precision of 0.911, with an area under the curve of 0.921. Conclusions: The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical exposure must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines for radiotherapy-computed tomography scans are available. This paper reviews the current literature on radiation dose optimization based on the effective dose and diagnostic reference level (DRL) for head, neck, and pelvic CT procedures used in radiation therapy planning. This paper explores the strategies used to optimize radiation doses, and high-quality images for diagnosis and treatment planning. Methods: A cross-sectional study was conducted on 300 patients with head, neck, and pelvic region cancer in our institution. The DRL, effective dose, volumetric CT dose index (CTDI_vol), and dose-length product (DLP) for the present and optimized protocol were calculated. DRLs were proposed for the DLP using the 75th percentile of the distribution. The DLP is a measure of the radiation dose received by a patient during a CT scan and is calculated by multiplying the CT dose index (CTDI) by the scan length. To calculate a DRL from a DLP, a large dataset of DLP values obtained from a specific imaging procedure must be collected and can be used to determine the median or 75th-percentile DLP value for each imaging procedure. Results: Significant variations were found in the DLP, CTDI_vol, and effective dose when we compared both the standard protocol and the optimized protocol. Also, the optimized protocol was compared with other diagnostic and radiotherapy CT scan studies conducted by other centers. As a result, we found that our institution’s DRL was significantly low. The optimized dose protocol showed a reduction in the CTDI_vol (70% and 63%), DLP (60% and 61%), and effective dose (67% and 62%) for both head, neck, and pelvic scans. Conclusions: Optimized protocol DRLs were proposed for comparison purposes.