Computer aided villi morphometric quantification in video-capsule enteroscopy: A newly developed software to quantify small bowel atrophy.

BACKGROUND AND AIMS: Small bowel capsule endoscopy (SBCE) has an established role in patients with non-responsive celiac disease (CeD). A non-invasive method to quantify small bowel atrophy is still lacking. METHODS: We analysed SBCE frames from CeD patients from 2018 to 2020. Histology was the reference standard, with atrophy defined as Marsh-Oberhuber score ≥ 3a. Three regions of interest (ROI) were blindly selected from each frame by an expert gastroenterologist and analysed using a National Institute of Health J image-processing software into a numerical scale. A 3D surface plot macro identified intestinal villi density through isolines plots. RESULTS: We acquired 306 ROIs from 57 frames with macroscopic atrophy and 45 with normal mucosa. Frames were classified as atrophic (n = 63) or non-atrophic (n = 39) per Marsh-Oberhuber classification. Median density score significantly differed between atrophic and non-atrophic frames (p < 0.001). The morphometric analysis showed a sensitivity of 77 % and a specificity of 79 % in discriminating between atrophic or non-atrophic mucosa with a 14.10 cut-off (Youden Index) and an overall AUC of 0.805 (CI 95 % 0.712-0.897). CONCLUSIONS: Our newly developed SBCE software can effectively quantify villous atrophy. Further studies are needed to validate its applicability in an external cohort.

Real-time automated quality control for quantitative MRI.

OBJECTIVE: This work presents an automated quality control (QC) system within quantitative MRI (qMRI) workflows. By leveraging the ISMRM/NIST quantitative MRI system phantom, we establish an open-source pipeline for rapid, repeatable, and accurate validation and stability tracking of sequence quantification performance across diverse clinical settings. MATERIALS AND METHODS: A microservice-based QC system for automated vial segmentation from quantitative maps was developed and tested across various MRF acquisition and protocol designs, with reports generated and returned to the scanner in real time. RESULTS: The system demonstrated consistent and repeatable value segmentation and reporting, successfully extracted all 252 T1 and T2 vial samples tested. Values extracted from the same sequence were found to be repeatable with 0.09% ± 1.23% and - 0.26% ± 2.68% intersession error, respectively. DISCUSSION: By providing real-time quantification performance assessment, this easily deployable automated QC approach streamlines sequence validation and long-term performance monitoring, vital for the broader acceptance of qMRI as a standard component of clinical protocols.

Towards dental diagnostic systems: Synergizing wavelet transform with generative adversarial networks for enhanced image data fusion.

The advent of precision diagnostics in pediatric dentistry is shifting towards ensuring early detection of dental diseases, a critical factor in safeguarding the oral health of the younger population. In this study, an innovative approach is introduced, wherein Discrete Wavelet Transform (DWT) and Generative Adversarial Networks (GANs) are synergized within an Image Data Fusion (IDF) framework to enhance the accuracy of dental disease diagnosis through dental diagnostic systems. Dental panoramic radiographs from pediatric patients were utilized to demonstrate how the integration of DWT and GANs can significantly improve the informativeness of dental images. In the IDF process, the original images, GAN-augmented images, and wavelet-transformed images are combined to create a comprehensive dataset. DWT was employed for the decomposition of images into frequency components to enhance the visibility of subtle pathological features. Simultaneously, GANs were used to augment the dataset with high-quality, synthetic radiographic images indistinguishable from real ones, to provide robust data training. These integrated images are then fed into an Artificial Neural Network (ANN) for the classification of dental diseases. The utilization of the ANN in this context demonstrates the system's robustness and culminates in achieving an unprecedented accuracy rate of 0.897, 0.905 precision, recall of 0.897, and specificity of 0.968. Additionally, this study explores the feasibility of embedding the diagnostic system into dental X-ray scanners by leveraging lightweight models and cloud-based solutions to minimize resource constraints. Such integration is posited to revolutionize dental care by providing real-time, accurate disease detection capabilities, which significantly reduces diagnostical delays and enhances treatment outcomes.

TRU-IMP: techniques for reliable use of images in medical physics; a graphical user interface to analyze and compare segmentations in nuclear medicine.

BACKGROUND: In the context of pharmacokinetic analyses, the segmentation method one uses has a large impact on the results obtained, thus the importance of transparency. Innovation: This paper introduces a graphical user interface (GUI), TRU-IMP, that analyzes time-activity curves and segmentations in dynamic nuclear medicine. This GUI fills a gap in the current technological tools available for the analysis of quantitative dynamic nuclear medicine image acquisitions. The GUI includes various techniques of segmentations, with possibilities to compute related uncertainties. Results: The GUI was tested on image acquisitions made on a dynamic nuclear medicine phantom. This allows the comparison of segmentations via their time-activity curves and the extracted pharmacokinetic parameters. Implications: The flexibility and user-friendliness allowed by the proposed interface make the analyses both easy to perform and adjustable to any specific case. This GUI permits researchers to better show and understand the reproducibility, precision, and accuracy of their work in quantitative dynamic nuclear medicine. Availability and Implementation: Source code freely available on GitHub: https://github.com/ArGilfea/TRU-IMP and location of the interface available from there. The GUI is fully compatible with iOS and Windows operating systems (not tested on Linux). A phantom acquisition is also available to test the GUI easily. .

Improved metal suppression using new generation low-field MRI: a biophantom feasibility study.

OBJECTIVE: Novel 0.55 MRI scanners have the potential to reduce metal artifacts around orthopedic implants. The purpose of this study was to compare metal artifact size and depiction of anatomy between 0.55 T and 3.0 T MRI in a biophantom. MATERIALS AND METHODS: Steel and titanium screws were implanted in 12 porcine knee specimens and imaging at 0.55 T and 3 T MRI was performed using the following sequences: turbo spin-echo (TSE), TSE with view angle tilting (VAT), and slice encoding for metal artifact correction (SEMAC) with proton-density (PD) and T2-weighted short-tau inversion-recovery (T2w-STIR) contrasts. Artifacts were measured, and visualization of anatomy (cartilage, bone, growth plates, cruciate ligaments) was assessed and compared between groups. RESULTS: Metal artifacts were significantly smaller at 0.55 T. The smallest artifact sizes were achieved with SEMAC at 0.55 T for both PD and T2w-STIR sequences; corresponding relative size reductions vs. 3.0 T were 78.7% and 79.4% (stainless steel) and 45.3% and 1.4% (titanium). Depiction of anatomical structures was superior at 0.55 T. CONCLUSION: Substantial reduction of artifact size resulting in superior depiction of anatomical structures is possible on novel 0.55 T MRI systems. Further clinical studies are required to elucidate patient-relevant advantages.

Early detection and spectral signature identification of tomato fungal diseases (Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum) by RGB and hyperspectral image analysis and machine learning.

Early identification of plant fungal diseases is critical for timely treatment, which can prevent significant agricultural losses. While molecular analysis offers high accuracy, it is often expensive and time-consuming. In contrast, image processing combined with machine learning provides a rapid and cost-effective alternative for disease diagnosis. This study presents a novel approach for detecting four common fungal diseases in tomatoes, Botrytis cinerea, Fusarium oxysporum, Alternaria alternata, and Alternaria solani, using both RGB (visible) and hyperspectral (400-950 nm) imaging of plant leaves over the first 11 days post-infection. Data sets were generated from leaf samples, and a range of statistical, texture, and shape features were extracted to train machine learning models. The spectral signatures of each disease were also developed for improved classification. The random forest model achieved the highest accuracy, with classification rates for RGB images of 65%, 71%, 75%, 77%, 83%, and 87% on days 1, 3, 5, 7, 9, and 11, respectively. For hyperspectral images, the classification accuracy increased from 86% on day 1 to 98% by day 11. Two- and three-dimensional spectral analyses clearly differentiated healthy plants from infected ones as early as day 3 for Botrytis cinerea. The Laplacian score method further highlighted key texture features, such as energy at 550 and 841 nm, entropy at 600 nm, correlation at 746 nm, and standard deviation at 905 nm, that contributed most significantly to disease detection. The method developed in this study offers a valuable and efficient tool for accelerating plant disease diagnosis and classification, providing a practical alternative to molecular techniques. .

Link between b.c.c.-f.c.c. orientation relationship and austenite morphology in CF8M stainless steel.

Slow-cooled CF8M duplex stainless steel is used for critical parts of the primary coolant pipes of nuclear reactors. This steel can endure severe service conditions, but it tends to become more brittle upon very long-term aging (tens of years). Therefore, it is essential to understand its specific microstructure and temporal evolution. As revealed by electron backscatter diffraction (EBSD) analyses, the microstructure consists of millimetre-scale ferritic grains within which austenite lath packets have grown with preferred crystallographic orientations concerning the parent ferritic phase far from the ferrite grain boundaries. In these lath packets where the austenite phase is nucleated, the lath morphology and crystal orientation accommodate the two ferrite orientations. Globally, the Pitsch orientation relationship appears to display the best agreement with the experimental data compared with other classical relationships. The austenite lath packets are parallel plate-shaped laths, characterized by their normal n. A novel methodology is introduced to elucidate the expected relationship between n and the crystallographic orientation given the coarse interfaces, even though n is only partly known from the observation surface, in contrast to the 3D crystal orientations measured by EBSD. The distribution of retrieved normals n is shown to be concentrated over a set of discrete orientations. Assuming that the ferrite and austenite obey the Pitsch orientation relationship, the determined lath normals are close to an invariant direction of the parent phase given by the same orientation relationship.

Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning.

Diagnosing dental caries poses a significant challenge in dentistry, necessitating precise and early detection for effective management. This study utilizes Self-Supervised Learning (SSL) tasks to improve the classification of dental caries in Cone Beam Computed Tomography (CBCT) images, employing the International Caries Detection and Assessment System (ICDAS). Faced with the challenge of scarce annotated medical images, our research employs SSL to utilize unlabeled data, thereby improving model performance. We have developed a pipeline incorporating unlabeled data extraction from CBCT exams and subsequent model training using SSL tasks. A distinctive aspect of our approach is the integration of image processing techniques with SSL tasks, along with exploring the necessity for unlabeled data. Our research aims to identify the most effective image processing techniques for data extraction, the most efficient deep learning architectures for caries classification, the impact of unlabeled dataset sizes on model performance, and the comparative effectiveness of different SSL approaches in this domain. Among the tested architectures, ResNet-18, combined with the SimCLR task, demonstrated an average F1-score macro of 88.42%, Precision macro of 90.44%, and Sensitivity macro of 86.67%, reaching a 5.5% increase in F1-score compared to models using only deep learning architecture. These results suggest that SSL can significantly enhance the accuracy and efficiency of caries classification in CBCT images.

Recent Advances in Intraoral Scanners.

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

For a clinical application of optical triangulation to assess respiratory rate using an RGB camera and a line laser.

This paper presents a non-contact and unrestrained respiration monitoring system based on the optical triangulation technique. The proposed system consists of a red-green-blue (RGB) camera and a line laser installed to face the frontal thorax of a human body. The underlying idea of the work is that the camera and line laser are mounted in opposite directions, unlike other research. By applying the proposed image processing algorithm to the camera image, laser coordinates are extracted and converted to world coordinates using the optical triangulation method. These converted world coordinates represent the height of the thorax of a person. The respiratory rate is measured by analyzing changes of the thorax surface depth. To verify system performance, the camera and the line laser are installed on the head and foot sides of a bed, respectively, facing toward the center of the bed. Twenty healthy volunteers were enrolled and underwent measurement for 100s. Evaluation results show that the optical triangulation-based image processing method demonstrates non-inferior performance to a commercial patient monitoring system with a root-mean-squared error of 0.30rpm and a maximum error of 1rpm ( p > 0.05 ), which implies the proposed non-contact system can be a useful alternative to the conventional healthcare method.

Enhancing pectus excavatum diagnosis with an automated batch evaluation tool for chest computed tomography images.

We aimed to implement a fully automatic computed tomography (CT) image-detection programming algorithm as a pectus excavatum (PE) diagnostic tool, facilitating comprehensive chest wall deformity evaluation. We developed our algorithm using MATLAB, leveraging the Hounsfield unit threshold and region growing methods. The MATLAB graphical user interface enables the direct use of our program. We validated the model using CT images of anthropomorphic phantoms and one normal individual. The measurement values obtained by our algorithm demonstrated very small differences compared to the known anthropomorphic phantom model data and manual measurement. For algorithm testing, 17,214 chest CT scans obtained from 57 PE patients were processed by the algorithm and independently reviewed by a radiologist and a thoracic surgeon. The measurements of transverse, anteroposterior, and sternum-to-vertebral distance of the thoracic cavity, along with the calculated data of four indices, exhibited high positive correlations (0.94-0.99). The asymmetry index and maximum anteroposterior hemithorax distance exhibited moderate correlation (0.40-0.83). Our automatic PE diagnostic tool demonstrated high accuracy; four chest wall deformity indices were obtained simultaneously without any initial manual marking, correlating well with manual measurements.

Deep learning-based defacing tool for CT angiography: CTA-DEFACE.

The growing use of artificial neural network (ANN) tools for computed tomography angiography (CTA) data analysis underscores the necessity for elevated data protection measures. We aimed to establish an automated defacing pipeline for CTA data. In this retrospective study, CTA data from multi-institutional cohorts were utilized to annotate facemasks (n = 100) and train an ANN model, subsequently tested on an external institution's dataset (n = 50) and compared to a publicly available defacing algorithm. Face detection (MTCNN) and verification (FaceNet) networks were applied to measure the similarity between the original and defaced CTA images. Dice similarity coefficient (DSC), face detection probability, and face similarity measures were calculated to evaluate model performance. The CTA-DEFACE model effectively segmented soft face tissue in CTA data achieving a DSC of 0.94 ± 0.02 (mean ± standard deviation) on the test set. Our model was benchmarked against a publicly available defacing algorithm. After applying face detection and verification networks, our model showed substantially reduced face detection probability (p < 0.001) and similarity to the original CTA image (p < 0.001). The CTA-DEFACE model enabled robust and precise defacing of CTA data. The trained network is publicly accessible at www.github.com/neuroAI-HD/CTA-DEFACE . RELEVANCE STATEMENT: The ANN model CTA-DEFACE, developed for automatic defacing of CT angiography images, achieves significantly lower face detection probabilities and greater dissimilarity from the original images compared to a publicly available model. The algorithm has been externally validated and is publicly accessible. KEY POINTS: The developed ANN model (CTA-DEFACE) automatically generates facemasks for CT angiography images. CTA-DEFACE offers superior deidentification capabilities compared to a publicly available model. By means of graphics processing unit optimization, our model ensures rapid processing of medical images. Our model underwent external validation, underscoring its reliability for real-world application.

Quantitative detection of hepatocyte mixture based on terahertz time-domain spectroscopy using spectral image analysis methods.

In recent years, terahertz (THz) technology has received widespread attention and has been leveraged to make breakthroughs in the field of bio-detection. However, studies on its application in mixtures have not yet been extensively conducted. Traditional one-dimensional (1D) spectral feature extraction methods are inefficient in terms of sensitivity and overall performance owing to spectral overlapping and distortions of a mixture. Thus, we adopted the Gramian angular field (GAF) method to map THz 1D spectra to two-dimensional (2D) images using correlation information between sequences. Image features of hepatocyte mixtures with different ratios were extracted using histogram of oriented gradients (HOGs) and gray level histograms (GLHs). A support vector regression (SVR) model was established for quantitative analysis. The method was more stable and accurate than principal component analysis (PCA) method, and RMSE and R2 values reached 0.072 and 0.932, respectively. This study enriches the algorithms of THz detection by combining the advantages of data upscaling and image processing, which is of great significance for the application of THz technology toward mixed-system detection.

Detection of body shape changes in obesity monitoring using image processing techniques.

Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking.

CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.

BACKGROUND: Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. METHODS: To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vectors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms). The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. RESULTS: This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achieving a true negative rate of 95% and a true positive rate of 97%. CONCLUSIONS: An image-based approach was developed to automatically evaluate the coloration quality of TBS. This finding highlights the potential of image-based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears.

Machine learning-based diagnostics of capsular invasion in thyroid nodules with wide-field second harmonic generation microscopy.

Papillary thyroid carcinoma (PTC) is one of the most common, well-differentiated carcinomas of the thyroid gland. PTC nodules are often surrounded by a collagen capsule that prevents the spread of cancer cells. However, as the malignant tumor progresses, the integrity of this protective barrier is compromised, and cancer cells invade the surroundings. The detection of capsular invasion is, therefore, crucial for the diagnosis and the choice of treatment and the development of new approaches aimed at the increase of diagnostic performance are of great importance. In the present study, we exploited the wide-field second harmonic generation (SHG) microscopy in combination with texture analysis and unsupervised machine learning (ML) to explore the possibility of quantitative characterization of collagen structure in the capsule and designation of different capsule areas as either intact, disrupted by invasion, or apt to invasion. Two-step k-means clustering showed that the collagen capsules in all analyzed tissue sections were highly heterogeneous and exhibited distinct segments described by characteristic ML parameter sets. The latter allowed a structural interpretation of the collagen fibers at the sites of overt invasion as fragmented and curled fibers with rarely formed distributed networks. Clustering analysis also distinguished areas in the PTC capsule that were not categorized as invasion sites by the initial histopathological analysis but could be recognized as prospective micro-invasions after additional inspection. The characteristic features of suspicious and invasive sites identified by the proposed unsupervised ML approach can become a reliable complement to existing methods for diagnosing encapsulated PTC, increase the reliability of diagnosis, simplify decision making, and prevent human-related diagnostic errors. In addition, the proposed automated ML-based selection of collagen capsule images and exclusion of non-informative regions can greatly accelerate and simplify the development of reliable methods for fully automated ML diagnosis that can be integrated into clinical practice.

Investigation of Rheological Properties of Molten Materials for Dripping Pills Based on Imaging Monitoring.

INTRODUCTION: Rheological properties, as critical material attributes (CMAs) of solid dispersion drugs such as dripping pills, affect the melting, dispersion, and solidification. Therefore, characterization and assessments of rheological properties in the pharmaceutical process are important in enhancing drug stability and bioavailability. OBJECTIVES: The study aimed to develop a method for analyzing the rheology of molten materials, assessing their consistency and how rheological properties affect the dripping process and pills quality. MATERIALS AND METHODS: The rheological behavior of molten materials composed of Ginkgo biloba leaf extract (GBE) and polyethylene glycol (PEG) 4000 was characterized. Batch consistency of molten materials was evaluated. Image monitoring technology was utilized to capture and process images of the droplet formation process. We established the relationship between the rheological properties of molten materials and various attributes. RESULTS: The quality consistency of molten materials was evaluated, with 12 batches showing similarity above 0.8. The MLR models showed strong correlations (R2 > 0.80) between rheological properties and evaluation attributes. The rheological properties, including consistency coefficient, flow index, and viscosity at 80°C, were identified as critical rheological properties of the molten materials. Rheological property differences of molten materials have an impact on the morphology of droplet and quality performance. CONCLUSION: A rheological method was established, enabling quality consistency evaluation of molten materials in dripping pills. This study revealed the influence of rheological properties on droplet formation process and dripping pills quality, providing a reference for researches on material attributes control of other traditional Chinese medicine dripping pills.

[Investigation of Imaging Conditions Using Human Body Equivalent Phantom and Neonatal Phantom in Portable Chest Radiography of Newborns].

PURPOSE: Investigation of imaging conditions using human body equivalent phantom and neonatal phantom in portable chest radiography of newborns. Although attempts have been made to reduce dose by image processing in portable X-ray radiography of neonates, no evaluation has been made at the raw data level of the images. In this study, we investigated dose reduction from the current imaging conditions using a simulated phantom and a neonatal phantom in terms of raw data level image quality and incident surface dose. METHODS: The pixel values of each region were calculated from chest photographs of newborn infants taken at 60 kV and 2.0 mAs, and the thickness and combination of acrylic, aluminum, and copper were adjusted to create a simulated phantom with equivalent pixel values. The SdNR and incident surface dose at each site obtained from the simulated phantom were compared to obtain imaging conditions equivalent to or better than 60 kV, 2.0 mAs. The neonatal phantom was imaged, and the CNR of the processed images was compared to that of 60 kV, 2.0 mAs. RESULTS: SdNR and incident surface dose results showed that 62 kV, 1.8 mAs was superior. Comparison with neonatal phantoms showed no significant difference. CONCLUSION: The simulated phantom was used to reproduce the clinical situation and to obtain excellent imaging conditions.

Computer-assisted evaluation of retinal vessel tortuosity in children with sickle cell disease without retinopathy.

OBJECTIVE: We assessed the predictive efficacy of automatically quantified retinal vascular tortuosity from the fundus pictures of patients with sickle cell disease (SCD) without evident retinopathy. METHODS: Retinal images were obtained from 31 healthy and 31 SCD participants using fundus imaging and analyzed using a novel computational automated metric assessment. The local and global vessel tortuosity and their relationship with systemic disease parameters were analyzed based on the images. RESULTS: SCD arteries had an increased local tortuosity index compared to the controls (0.0007 ± 0.0019 vs. 0.0006 ± 0.0014, p = 0.019). Furthermore, the SCD patients had wider vessel caliber mainly in the arteries (14.68 ± 5.3 vs. 14.06 ± 5.3, p < 0.001). The SCD global tortuosity did not differ significantly from that of the controls (p = 0.598). The female participants had significantly reduced retinal vessel tortuosity indices compared to the male participants (p = 0.018). CONCLUSION: Retinal arterial tortuosity and caliber were reliable and objective measures that could be used as a non-invasive prognostic and diagnostic indicator in sickle cell retinopathy. Further studies are required to correlate these local vascular parameters to systemic risk factors and monitor their progression and change over time.

Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.