Recent advancements in image-enhanced endoscopy in the pancreatobiliary field.

Image-enhanced endoscopy (IEE) has advanced gastrointestinal disease diagnosis and treatment. Traditional white-light imaging has limitations in detecting all gastrointestinal diseases, prompting the development of IEE. In this review, we explore the utility of IEE, including texture and color enhancement imaging and red dichromatic imaging, in pancreatobiliary (PB) diseases. IEE includes methods such as chromoendoscopy, optical-digital, and digital methods. Chromoendoscopy, using dyes such as indigo carmine, aids in delineating lesions and structures, including pancreato-/cholangio-jejunal anastomoses. Optical-digital methods such as narrow-band imaging enhance mucosal details and vessel patterns, aiding in ampullary tumor evaluation and peroral cholangioscopy. Moreover, red dichromatic imaging with its specific color allocation, improves the visibility of thick blood vessels in deeper tissues and enhances bleeding points with different colors and see-through effects, proving beneficial in managing bleeding complications post-endoscopic sphincterotomy. Color enhancement imaging, a novel digital method, enhances tissue texture, brightness, and color, improving visualization of PB structures, such as PB orifices, anastomotic sites, ampullary tumors, and intraductal PB lesions. Advancements in IEE hold substantial potential in improving the accuracy of PB disease diagnosis and treatment. These innovative techniques offer advantages paving the way for enhanced clinical management of PB diseases. Further research is warranted to establish their standard clinical utility and explore new frontiers in PB disease management.

Breathing motion compensation in chest tomosynthesis: evaluation of the effect on image quality and presence of artifacts.

Purpose: Chest tomosynthesis (CTS) has a relatively longer acquisition time compared with chest X-ray, which may increase the risk of motion artifacts in the reconstructed images. Motion artifacts induced by breathing motion adversely impact the image quality. This study aims to reduce these artifacts by excluding projection images identified with breathing motion prior to the reconstruction of section images and to assess if motion compensation improves overall image quality. Approach: In this study, 2969 CTS examinations were analyzed to identify examinations where breathing motion has occurred using a method based on localizing the diaphragm border in each of the projection images. A trajectory over diaphragm positions was estimated from a second-order polynomial curve fit, and projection images where the diaphragm border deviated from the trajectory were removed before reconstruction. The image quality between motion-compensated and uncompensated examinations was evaluated using the image quality criteria for anatomical structures and image artifacts in a visual grading characteristic (VGC) study. The resulting rating data were statistically analyzed using the software VGC analyzer. Results: A total of 58 examinations were included in this study with breathing motion occurring either at the beginning or end ( n = 17 ) or throughout the entire acquisition ( n = 41 ). In general, no significant difference in image quality or presence of motion artifacts was shown between the motion-compensated and uncompensated examinations. However, motion compensation significantly improved the image quality and reduced the motion artifacts in cases where motion occurred at the beginning or end. In examinations where motion occurred throughout the acquisition, motion compensation led to a significant increase in ripple artifacts and noise. Conclusions: Compensation for respiratory motion in CTS by excluding projection images may improve the image quality if the motion occurs mainly at the beginning or end of the examination. However, the disadvantages of excluding projections may outweigh the benefits of motion compensation.

Endoscopic detection and diagnosis of gastric cancer using image-enhanced endoscopy: A systematic review and meta-analysis.

Objectives: We aimed to conduct a systematic review and meta-analysis to assess the value of image-enhanced endoscopy including blue laser imaging (BLI), linked color imaging, narrow-band imaging (NBI), and texture and color enhancement imaging to detect and diagnose gastric cancer (GC) compared to that of white-light imaging (WLI). Methods: Studies meeting the inclusion criteria were identified through PubMed, Cochrane Library, and Japan Medical Abstracts Society databases searches. The pooled risk ratio for dichotomous variables was calculated using the random-effects model to assess the GC detection between WLI and image-enhanced endoscopy. A random-effects model was used to calculate the overall diagnostic performance of WLI and magnifying image-enhanced endoscopy for GC. Results: Sixteen studies met the inclusion criteria. The detection rate of GC was significantly improved in linked color imaging compared with that in WLI (risk ratio, 2.20; 95% confidence interval [CI], 1.39-3.25; p < 0.01) with mild heterogeneity. Magnifying endoscopy with NBI (ME-NBI) obtained a pooled sensitivity, specificity, and area under the summary receiver operating curve of 0.84 (95 % CI, 0.80-0.88), 0.96 (95 % CI, 0.94-0.97), and 0.92, respectively. Similarly, ME-BLI showed a pooled sensitivity, specificity, and area under the curve of 0.81 (95 % CI, 0.77-0.85), 0.85 (95 % CI, 0.82-0.88), and 0.95, respectively. The diagnostic efficacy of ME-NBI/BLI for GC was evidently high compared to that of WLI, However, significant heterogeneity among the NBI studies still existed. Conclusions: Our meta-analysis showed a high detection rate for linked color imaging and a high diagnostic performance of ME-NBI/BLI for GC compared to that with WLI.

Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.

Significance: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed. Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system. Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼ 35 dB (SNR), ∼ 8.65 a . u . (contrast), and ∼ 0.67 a . u . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Characterization of Foodborne Pathogens in Biofilms: A Four-Dimensional Structural Dynamics Approach Using HCS-CLSM.

The functional properties of biofilms are intimately related to their spatial architecture. Structural data are therefore of prime importance to dissect the complex social and survival strategies of biofilms and ultimately to improve their control. Confocal laser scanning microscopy (CLSM) is the most widespread microscopic tool to decipher biofilm structure, enabling noninvasive three-dimensional investigation of their dynamics down to the single-cell scale. The emergence of fully automated high content screening (HCS) systems, associated with large-scale image analysis, has radically amplified the flow of available biofilm structural data. In this contribution, we present a HCS-CLSM protocol used to analyze biofilm four-dimensional structural dynamics at high throughput. Meta-analysis of the quantitative variables extracted from HCS-CLSM will contribute to a better biological understanding of biofilm traits.

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.

Application of machine vision in food computing: A review.

With global intelligence advancing and the awareness of sustainable development growing, artificial intelligence technology is increasingly being applied to the food industry. This paper, grounded in practical application cases, reviews the current research status and prospects of machine vision-based image recognition technology in food computing. It explores the general workflow of image recognition, applications based on traditional machine learning and deep learning methods. The paper covers areas including food safety detection, dietary nutrition analysis, process monitoring, and enterprise management model optimization. The aim is to provide a solid theoretical foundation and technical guidance for the integration and cross-fertilization of the food industry with artificial intelligence technology.

Harmonizing Unets: Attention Fusion module in cascaded-Unets for low-quality OCT image fluid segmentation.

Optical coherence tomography (OCT) is widely used for its high resolution. Accurate OCT image segmentation can significantly improve the diagnosis and treatment of retinal diseases such as Diabetic Macular Edema (DME). However, in resource-limited regions, portable devices with low-quality output are more frequently used, severely affecting the performance of segmentation. To address this issue, we propose a novel methodology in this paper, including a dedicated pre-processing pipeline and an end-to-end double U-shaped cascaded architecture, H-Unets. In addition, an Adaptive Attention Fusion (AAF) module is elaborately designed to improve the segmentation performance of H-Unets. To demonstrate the effectiveness of our method, we conduct a bunch of ablation and comparative studies on three open-source datasets. The experimental results show the validity of the pre-processing pipeline and H-Unets, achieving the highest Dice score of 90.60%±0.87% among popular methods in a relatively small model size.

A novel GAN-based three-axis mutually supervised super-resolution reconstruction method for rectal cancer MR image.

BACKGROUND AND OBJECTIVE: This study aims to enhance the resolution in the axial direction of rectal cancer magnetic resonance (MR) imaging scans to improve the accuracy of visual interpretation and quantitative analysis. MR imaging is a critical technique for the diagnosis and treatment planning of rectal cancer. However, obtaining high-resolution MR images is both time-consuming and costly. As a result, many hospitals store only a limited number of slices, often leading to low-resolution MR images, particularly in the axial plane. Given the importance of image resolution in accurate assessment, these low-resolution images frequently lack the necessary detail, posing substantial challenges for both human experts and computer-aided diagnostic systems. Image super-resolution (SR), a technique developed to enhance image resolution, was originally applied to natural images. Its success has since led to its application in various other tasks, especially in the reconstruction of low-resolution MR images. However, most existing SR methods fail to account for all anatomical planes during reconstruction, leading to unsatisfactory results when applied to rectal cancer MR images. METHODS: In this paper, we propose a GAN-based three-axis mutually supervised super-resolution reconstruction method tailored for low-resolution rectal cancer MR images. Our approach involves performing one-dimensional (1D) intra-slice SR reconstruction along the axial direction for both the sagittal and coronal planes, coupled with inter-slice SR reconstruction based on slice synthesis in the axial direction. To further enhance the accuracy of super-resolution reconstruction, we introduce a consistency supervision mechanism across the reconstruction results of different axes, promoting mutual learning between each axis. A key innovation of our method is the introduction of Depth-GAN for synthesize intermediate slices in the axial plane, incorporating depth information and leveraging Generative Adversarial Networks (GANs) for this purpose. Additionally, we enhance the accuracy of intermediate slice synthesis by employing a combination of supervised and unsupervised interactive learning techniques throughout the process. RESULTS: We conducted extensive ablation studies and comparative analyses with existing methods to validate the effectiveness of our approach. On the test set from Shanxi Cancer Hospital, our method achieved a Peak Signal-to-Noise Ratio (PSNR) of 34.62 and a Structural Similarity Index (SSIM) of 96.34 %. These promising results demonstrate the superiority of our method.

Overexpression of SEZ6L2 and Immune Infiltration in Cancer Based on Gene Image Diagnosis.

BACKGROUND: With the rapid advancement of optical image diagnostic technology, researchers are delving into the potential applications in the field of cancer diagnosis and treatment. The exact link between the SEZ6L2 gene and cancer immune infiltration remains elusive. MATERIALS AND METHODS: This study aims to investigate the relationship between SEZ6L2 gene overexpression and cancer immune infiltration using optical image diagnostic technology, thereby presenting novel insights for enhancing cancer diagnosis and treatment strategies. Tissue samples obtained from cancer patients were meticulously analyzed to quantitatively assess the expression of the SEZ6L2 gene through light image diagnostic technology. Additionally, immunohistochemical techniques were employed to assess the nature and quantity of immune infiltrating cells within the cancerous tissues. RESULTS: The enrichment pathways were found to include complement activation, circulating immunoglobulin mediated humoral immune response, protein activation cascade, immunoglobulin complex, and immunoglobulin. In addition, the expression of SEZ6L2 is closely related to the infiltration level of tumor infiltrating immune cells (TIICs), and there is a potential relationship between the expression of SEZ6L2 and different marker genes of TIIC. CONCLUSION: Increased SEZ6L2 mRNA expression in breast invasive carcinoma was significantly associated with negative prognosis and immune invasion. SEZ6L2 may be a novel prognostic biomarker and a potential immunotherapeutic target in BRCA.

CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.

Breast cancer is a significant global public health concern, with various treatment options available based on tumor characteristics. Pathological examination of excision specimens after surgery provides essential information for treatment decisions. However, the manual selection of representative sections for histological examination is laborious and subjective, leading to potential sampling errors and variability, especially in carcinomas that have been previously treated with chemotherapy. Furthermore, the accurate identification of residual tumors presents significant challenges, emphasizing the need for systematic or assisted methods to address this issue. In order to enable the development of deep-learning algorithms for automated cancer detection on radiology images, it is crucial to perform radiology-pathology registration, which ensures the generation of accurately labeled ground truth data. The alignment of radiology and histopathology images plays a critical role in establishing reliable cancer labels for training deep-learning algorithms on radiology images. However, aligning these images is challenging due to their content and resolution differences, tissue deformation, artifacts, and imprecise correspondence. We present a novel deep learning-based pipeline for the affine registration of faxitron images, the x-ray representations of macrosections of ex-vivo breast tissue, and their corresponding histopathology images of tissue segments. The proposed model combines convolutional neural networks and vision transformers, allowing it to effectively capture both local and global information from the entire tissue macrosection as well as its segments. This integrated approach enables simultaneous registration and stitching of image segments, facilitating segment-to-macrosection registration through a puzzling-based mechanism. To address the limitations of multi-modal ground truth data, we tackle the problem by training the model using synthetic mono-modal data in a weakly supervised manner. The trained model demonstrated successful performance in multi-modal registration, yielding registration results with an average landmark error of 1.51 mm (±2.40), and stitching distance of 1.15 mm (±0.94). The results indicate that the model performs significantly better than existing baselines, including both deep learning-based and iterative models, and it is also approximately 200 times faster than the iterative approach. This work bridges the gap in the current research and clinical workflow and has the potential to improve efficiency and accuracy in breast cancer evaluation and streamline pathology workflow.

Artificial vascular image generation using blood vessel texture maps.

BACKGROUND: Current methods for identifying blood vessels in digital images typically involve training neural networks on pixel-wise annotated data. However, manually outlining whole vessel trees in images tends to be very costly. One approach for reducing the amount of manual annotation is to pre-train networks on artificially generated vessel images. Recent pre-training approaches focus on generating proper artificial geometries for the vessels, while the appearance of the vessels is defined using general statistics of the real samples or generative networks requiring an additional training procedure to be defined. In contrast, we propose a methodology for generating blood vessels with realistic textures extracted directly from manually annotated vessel segments from real samples. The method allows the generation of artificial images having blood vessels with similar geometry and texture to the real samples using only a handful of manually annotated vessels. METHODS: The first step of the method is the manual annotation of the borders of a small vessel segment, which takes only a few seconds. The annotation is then used for creating a reference image containing the texture of the vessel, called a texture map. A procedure is then defined to allow texture maps to be placed on top of any smooth curve using a piecewise linear transformation. Artificial images are then created by generating a set of vessel geometries using Bézier curves and assigning vessel texture maps to the curves. RESULTS: The method is validated on a fluorescence microscopy (CORTEX) and a fundus photography (DRIVE) dataset. We show that manually annotating only 0.03% of the vessels in the CORTEX dataset allows pre-training a network to reach, on average, a Dice score of 0.87 ± 0.02, which is close to the baseline score of 0.92 obtained when all vessels of the training split of the dataset are annotated. For the DRIVE dataset, on average, a Dice score of 0.74 ± 0.02 is obtained by annotating only 0.29% of the vessels, which is also close to the baseline Dice score of 0.81 obtained when all vessels are annotated. CONCLUSION: The proposed method can be used for disentangling the geometry and texture of blood vessels, which allows a significant improvement of network pre-training performance when compared to other pre-training methods commonly used in the literature.

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.

GLCV-NET: An automatic diagnosis system for advanced liver fibrosis using global-local cross view in B-mode ultrasound images.

BACKGROUND AND OBJECTIVE: Advanced liver fibrosis is a critical stage in the evaluation of chronic liver disease (CLD), holding clinical significance in the development of treatment strategies and estimating the disease progression. METHODS: This paper proposes an innovative Global-Local Cross-View Network (GLCV-Net) for the automatic diagnosis of advanced liver fibrosis from ultrasound (US) B-mode images. The proposed method consists of three main components: 1. A Segmentation-enhanced Global Hybrid Feature Extractor for segmenting the liver parenchyma and extracting global features; 2. A Heatmap-weighted Local Feature Extractor for selecting candidate regions and automatically identifying suspicious areas to construct local features; 3. A Scale-adaptive Fusion Module to balance the contributions of global and local scales in evaluating advanced liver fibrosis. RESULTS: The predictive performance of the model was validated on an internal dataset of 1003 chronic liver disease (CLD) patients and an external dataset of 46 CLD patients, both subjected to liver fibrosis staging through pathological assessment. On the internal dataset, GLCV-Net achieved 86.9% accuracy, 85.0% recall, 85.4% precision, and 85.2% F1-score. Further validation on the external dataset confirmed its robustness, with scores of 86.1% in accuracy, 83.1% in recall, 80.8% in precision, and 81.9% in F1-score. CONCLUSION: These results underscore the GLCV-Net's potential as a promising approach for non-invasively and accurately diagnosing advanced liver fibrosis in CLD patients, breaking through the limitations of traditional methods by integrating global and local information of liver fibrosis, significantly enhancing diagnostic accuracy.

Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness.

INTRODUCTION: Low contrast resolution in abdominal computed tomography (CT) may be negatively affected by attempts to lower patient doses. Iterative reconstruction (IR) algorithms play a key role in mitigating this problem. The reconstructed slice thickness also influences image quality. The aim was to assess the interaction and influence of patient dose, slice thickness, and IR strength on image quality in abdominal CT. METHOD: With a simultaneous acquisition, images at 42 and 98 mAs were obtained in 25 patients. Multiplanar images with slice thicknesses of 1, 2, and 3 mm and advanced modeled iterative reconstruction (ADMIRE) strengths of 3 (AD3) and 5 (AD5) were reconstructed. Four radiologists evaluated the images in a pairwise manner based on five image criteria. Ordinal logistic regression with mixed effects was used to evaluate the effect of tube load, ADMIRE strength, and slice thickness using the visual grading regression technique. RESULTS: For all assessed image criteria, the regression analysis showed significantly (p < 0.001) higher image quality for AD5, but lower for tube load 42 mAs, and slice thicknesses of 1 mm and 2 mm, compared to the reference categories of AD3, 98 mAs, and 3 mm, respectively. AD5 at 2 mm was superior to AD3 at 3 mm for all image criteria studied. AD5 1 mm produced inferior image quality for liver parenchyma and overall image quality compared to AD3 3 mm. Interobserver agreement (ICC) ranged from 0.874 to 0.920. CONCLUSION: ADMIRE 5 at 2 mm slice thickness may allow for further dose reductions due to its superiority when compared to ADMIRE 3 at 3 mm slice thickness. IMPLICATIONS FOR PRACTICE: Combination of thinner slices and higher ADMIRE strength facilitates imaging at low dose.

Body image and quality of life undergoing totally robotic versus robotic-assisted distal gastrectomy: a retrospective propensity score matched cohort study.

BACKGROUND: With the improvement of anastomotic techniques and the iteration of anastomotic instruments, robotic intracorporeal suturing has become increasingly proficient. The era of fully intracorporeal anastomosis in robotic gastric cancer resection is emerging. This study aims to explore the impact of totally robotic distal gastrectomy (TRDG) and robotic-assisted distal gastrectomy (RADG) on patients' quality of life. PATIENTS AND METHODS: This study is a comparative retrospective study of propensity score matching. This study included 306 patients who underwent robotic distal gastrectomy for gastric cancer between June 2016 and December 2023 at our center. Covariates used in the propensity score included sex, age, BMI, ASA score, maximum tumour diameter, degree of histological differentiation, Pathological TNM stage, Pathological T stage, Pathological N stage, and Lauren classification. Outcome measures included operative time, intraoperative bleeding, time to first venting, time to first fluid intake, postoperative hospital stay, total hospitalization cost, total length of abdominal incision, postoperative complications, inflammatory response, body image, and quality of life. RESULTS: According to the results of the study, compared with the RADG group, the TRDG group had a faster recovery time for gastrointestinal function (P = 0.025), shorter length of abdominal incision (P < 0.001), fewer days in the hospital (P = 0.006) less pain (P < 0.001), less need for additional analgesia (P = 0.013), and a postoperative white blood cell count (P < 0.001) and C-reactive protein content indexes were lower (P<0.001). In addition, the TRDG group had significantly better body imagery and cosmetic scores (P = 0.015), physical function (P = 0.039), role function (P = 0.046), and global function (P = 0.021) than the RARS group. Meanwhile, the TRDG group had milder symptoms of fatigue (P = 0.037) and pain (P < 0.001). The PASQ Total Subscale Score (P < 0.001) and Global Subscale Score (P < 0.001) were significantly lower in the TRDG group than in the RADG group at postoperative 3 months. CONCLUSION: Totally robotic distal gastrectomy has a smaller incision, faster gastrointestinal recovery time, fewer days of postoperative hospitalization, and lower inflammatory markers than robotic-assisted distal gastrectomy. At the same time, postoperative cosmetic and quality of life outcomes were satisfactory. Clinically, these benefits translate to enhanced patient recovery, reduced surgical trauma, and better postoperative outcomes. These findings could guide surgeons in selecting more effective surgical approaches for patients undergoing gastrectomy, leading to better overall patient satisfaction and outcomes.

Risk-benefit analysis of a multi-site radiographer comment model for emergency departments.

OBJECTIVES: Critical/urgent X-ray findings are not always communicated in an appropriate time frame to ED physicians. The practice of radiographers alerting referrers to clinically significant image findings (verbally, via image flags or written comment) is noted internationally but risk assessment data is unavailable in the literature. A hybrid radiographer comment and alert model was piloted in New South Wales and a risk-benefit assessment conducted for timely and safe communication of abnormal X-ray appearances to ED physicians. METHODS: Radiographer comments (n = 1102) were provided to five New South Wales EDs by 69 radiographers for a period of 3-12 months. Site auditors classified comments as true positive (TP), false positive (FP) or indeterminate (ID) with respect to the radiology report. FP comments were investigated with ED referrers and a low-medium-high-risk assessment was provided by two independent reviewers. RESULTS: A total of 42 FP (3.9%; 95% confidence interval [CI] 2.9-5.3) comments were analysed for any adverse outcomes. Risk assessments demonstrated 37 low, 5 low-moderate and no high-risk cases. A total of 282 direct or potential patient benefits were identified (26.4%; 95% CI 23.8-29.1). A total of 42 radiology report discrepancies were incidentally found: (3.9%; 95% CI 2.9-5.3). Audit results demonstrated areas where the radiographer comment could mitigate risk. CONCLUSION: The provision of radiographer alerts with a written comment for ED was found to be low risk to patients in the pilot study. Radiographers communicating directly with the emergency team when abnormal image appearances are detected can reduce diagnostic error and improve patient safety and health outcomes.

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.