Accuracy of an artificial intelligence-enabled diagnostic assistance device in recognizing normal chest radiographs: a service evaluation.

Objectives: Artificial intelligence (AI) enabled devices may be able to optimize radiologists' productivity by identifying normal and abnormal chest X-rays (CXRs) for triaging. In this service evaluation, we investigated the accuracy of one such AI device (qXR). Methods: A randomly sampled subset of general practice and outpatient-referred frontal CXRs from a National Health Service Trust was collected retrospectively from examinations conducted during November 2022 to January 2023. Ground truth was established by consensus between 2 radiologists. The main objective was to estimate negative predictive value (NPV) of AI. Results: A total of 522 CXRs (458 [87.74%] normal CXRs) from 522 patients (median age, 64 years [IQR, 49-77]; 305 [58.43%] female) were analysed. AI predicted 348 CXRs as normal, of which 346 were truly normal (NPV: 99.43% [95% CI, 97.94-99.93]). The sensitivity, specificity, positive predictive value, and area under the ROC curve of AI were found to be 96.88% (95% CI, 89.16-99.62), 75.55% (95% CI, 71.34-79.42), 35.63% (95% CI, 28.53-43.23), and 91.92% (95% CI, 89.38-94.45), respectively. A sensitivity analysis was conducted to estimate NPV by varying assumptions of the prevalence of normal CXRs. The NPV ranged from 88.96% to 99.54% as prevalence increased. Conclusions: The AI device recognized normal CXRs with high NPV and has the potential to increase radiologists' productivity. Advances in knowledge: There is a need for more evidence on the utility of AI-enabled devices in identifying normal CXRs. This work adds to such limited evidence and enables researchers to plan studies to further evaluate the impact of such devices.

Clinical and in vitro application of robotic computer-assisted implant surgery: a scoping review.

In recent years, the emergence and application of robotic computer-assisted implant surgery (r-CAIS) has resulted in a revolutionary shift in conventional implant diagnosis and treatment. This scoping review was performed to verify the null hypothesis that r-CAIS has a relatively high accuracy of within 1 mm, with relatively few complications and a short operative time. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). From the 3355 publications identified in the PubMed, Scopus, Web of Science, and Google Scholar databases, 28 were finally included after a comprehensive review and analysis. The null hypothesis is partly accepted, as r-CAIS has a relatively high accuracy (coronal and apical deviation within 1 mm), and no significant adverse events or complications have been reported to date, although additional confirmatory studies are needed. However, there is insufficient evidence for a shorter surgical time, and further clinical research on this topic is required.

Novel use of dynamic navigation for guiding a piezoelectric device during window osteotomy for maxillary sinus floor elevation in complex clinical scenarios.

In patients with severe atrophy of the posterior maxilla requiring lateral maxillary sinus floor elevation (MSFE), the window location and size are commonly designed according to the future implants and anatomical conditions. A window osteotomy becomes challenging when there is an extended edentulous space in the maxilla with no reference from the natural dentition, or when the surgical site involves anatomical variations, for example in the course of a large vessel or a sinus septum. Through preoperative planning and real-time visualization, the application of dynamic navigation allows an accurate location, optimal dimension, and customized shape during lateral window osteotomy. This article introduces a digital protocol for ensuring an accurate and safe window osteotomy for MSFE in complex clinical scenarios, by integrating dynamic navigation and a piezoelectric device.

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.

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.

Structure-based virtual screening towards the discovery of novel thrombin inhibitors with Anti-HCC activities.

Introduction: Hepatic carcinoma (HCC) is one of the most lethal malignant tumors in the world, and new treatment regimens for this disease are urgently needed. Studies have shown that thrombin stimulates tumor progression by forming fibrin and activating platelets. Dabigatran etexilate, a thrombin inhibitor, can inhibit the activity of thrombin and prevent the proliferation and metastasis of HCC in cells and nude mice. Methods: The present study was designed to find thrombin inhibitors with novel skeletons, and further confirm the correlation between thrombin inhibition and HCC prevention to identify potential anti-HCC drug leads. Results and Discussion: The potential thrombin inhibitors were firstly screened in the Topscience Database, and 20 potential active molecules were found by molecular docking. The effect of these molecules on thrombin inhibition, coagulation and tumor proliferation were evaluated, and the definite activity of ZXX-4 was identified. Further in vivo assays in nude mice showed that ZXX-4 inhibited tumor proliferation in nude mice, reduced tumor metastasis, and enhanced the clinical efficacy of first-line drug sorafenib for the treatment of HCC. ZXX-4 can be further explored as an anti-tumor lead compound with a novel skeleton, and inhibition of thrombin can serve as a potential treatment strategy for HCC.

From past to digital time: Bibliometric perspective of worldwide research productivity on robotic and computer-assisted arthroplasty.

Objective: The number of citations can be used as an impact marker of research work. This study aimed to determine and characterize the worldwide research productivity on robotic and computer-assisted arthroplasty. Methods: All accessible publications from 1992 to 2023 on robotic and computer-assisted arthroplasty from Web of Science Core Collection (WOSCC) database were recorded in August 2024. The following aspects were retrieved: cited times, name of author, keywords, institution, country, year of publication, journal, title, topic, impact factor, and H-index. VOSviewer software and Microsoft Excel were conducted to make the bibliometric research visual. The nature of our study is a systematic study and was conducted in China. Results: 1061 articles were included in our study. The total cited times were 27,461 with the average number of 26. The most productive year was 2022, with a total of 158 publications. The United States contributed the highest number of articles (n = 389, 36.66%) and the Hospital for Special Surgery (n = 53, 5.00%) held the leading institution. "Orthopedics" became the dominant topic (n = 894, 84.26%) and the latest keywords "clinical outcomes", "acetabular cup placement", and "satisfaction" have mainly appeared since 2020. Conclusions: Our analysis gives a comprehensive review of related articles on robotic and computer-assisted arthroplasty from past to future. The United States dominated studies of robotic and computer-assisted arthroplasty and a journal about arthroplasty was the most productive one. "Clinical outcomes", "Acetabular cup placement", and "Satisfaction" may become the future research hotspots.

Accuracy of robotic arm-assisted versus computed tomography-based navigation in total hip arthroplasty using the direct anterior approach: a retrospective study.

BACKGROUND: A robotic arm-assisted and a computed tomography (CT)- based navigation system have been reported to improve the accuracy of component positioning in total hip arthroplasty (THA). However, no study has compared robotic arm-assisted THA (rTHA) to CT-based navigated THA (nTHA) concerning accuracy of cup placement and acetabular fractures using the direct anterior approach (DAA). This study aimed to compare the accuracy of cup placement and the presence of intraoperative acetabular fractures between rTHA and nTHA using DAA in the supine position. METHODS: We retrospectively investigated 209 hips of 188 patients who underwent rTHA or nTHA using DAA (rTHA using the Mako system: 85 hips of 79 patients; nTHA: 124 hips of 109 patients). After propensity score matching for age and sex, each group consisted of 73 hips. We evaluated clinical and radiographic outcomes, comparing postoperative cup orientation and position, measured using a three-dimensional templating software, to preoperative CT planning. Additionally, we investigated the prevalence of occult acetabular fracture. RESULTS: Clinical outcomes were not significantly different between the groups at 1 year postoperatively. The mean absolute error of cup orientation was significantly smaller in the rTHA group than in nTHA (inclination: 1.4° ± 1.2° vs. 2.7° ± 2.2°, respectively; p = 0.0001, anteversion: 1.5° ± 1.3° vs. 2.2° ± 1.7°, respectively; p = 0.007). The cases within an absolute error of 5 degrees in both RI and RA were significantly higher in the rTHA (97.3%) than in nTHA group (82.2%) (p = 0.003). The absolute error of the cup position was not significantly different between the two groups. The prevalence of occult acetabular fracture did not differ significantly between the two groups (rTHA: n = 0 [0%] vs. nTHA: n = 1 [1.4%]). CONCLUSION: Cup placement using DAA in the supine position in rTHA was more accurate with fewer outliers compared to nTHA. Therefore, rTHA performed via DAA in a supine position would be useful for accurate cup placement.

Are Sleeves Necessary in Static Computer-Assisted Implant Surgery? A Comparative In Vitro Analysis.

OBJECTIVES: This study aims to examine differences in trueness and precision between surgical guides with (S) and without sleeves (SL). A secondary aim was to assess the impact of the sleeve-to-bone distance. MATERIALS AND METHODS: Mandible replicas (n = 120) were printed from an STL file obtained from a clinical CBCT. The mandibles were divided into sleeved (S, n = 60) and sleeveless (SL, n = 60) groups, each further divided into three categories (n = 20 each) with different heights from the guide to the implant platform: 2 mm (H2), 4 mm (H4), or 6 mm (H6). Digital planning and surgical guide design were done for a 4.1 × 10 mm implant for site #30. Post-op positions were captured using a scan body and lab scanner. Angular deviation was the primary outcome, with 3D and 2D deviations as secondary parameters. Statistical analysis included two-sample t-tests, and one-way and two-way ANOVA. RESULTS: Group S (2.41 ± 1.41°) had significantly greater angular deviation than Group SL (1.65 ± 0.93°; p = 0.0001). Angular deviation increased with sleeve-to-bone distance. H2 deviations were 1.48 ± 0.80° (S) vs. 1.02 ± 0.45° (SL; p < 0.05), H4: 2.36 ± 1.04° (S) vs. 1.48 ± 0.79° (SL; p < 0.05), H6: 3.37 ± 0.67° (S) vs. 2.46 ± 0.89° (SL; p < 0.05). 3D deviation at the implant platform was 0.36 ± 0.17 mm (S) vs. 0.30 ± 0.15 mm (SL; p < 0.05) and at the apex 0.74 ± 0.34 mm (S) vs. 0.53 ± 0.31 mm (SL; p < 0.01). Group SL at H2 had the smallest inter-implant distance (0.53 ± 0.37°), while Group S at H4 had the largest (1.20 ± 0.84°; p < 0.05). CONCLUSIONS: Sleeveless guides are more accurate than sleeved guides, and angular deviation is influenced by the distance from the guide to the implant platform.

Comparing Audiological Outcomes of Conventional and AI-Upgraded Cochlear Implant Speech Processors.

In current age of technology, artificial intelligence is used in the medical field to improve the quality and accuracy in patient care and achieve better clientele satisfaction. The use of artificial intelligence in the field of hearing rehabilitation and cochlear implantation has an immense scope and it enhances the accuracy in placement of electrode array, forecasting site of surgical location and optimization of speech processing. This study aims to compare the audiological outcomes of conventional versus artificial intelligence technology enabled cochlear implant speech processors. Additionally, it compares the individual performance and satisfaction level with use of both types of speech processors. All children who underwent upgradation of their cochlear implant speech processors at a tertiary care cochlear implant centre with artificial intelligence enabled speech processors were included in the study. The comparison of audiological outcomes of conventional versus artificial intelligence integrated speech processors were assessed by using Aided Audiometry, Categories of Auditory Perception Score and Speech Intelligibility Rating scale. Children using the basic model cochlear implant speech processor which was provided at the time of implantation are referred as conventional cochlear implant speech processor user. Their speech processors were subsequently upgraded with current generation artificial intelligence integrated speech processors which is referred here as artificial intelligence upgraded cochlear implant speech processor. During the study, a total of thirty-four (34) patients underwent upgradation of cochlear implant speech processors. The mean categories of auditory perception score were 11.58 and 11.94 using conventional and artificial intelligence upgraded speech processor respectively. The mean speech intelligibility rating score was 4.5 and 4.6 respectively. The audiological outcomes of conventional speech processors are comparable with those using artificial intelligence enabled speech processors. However, the clientele satisfaction in respect to quality of sound, ease of listening in difficult listening environment, smart connectivity options for both phone and television is available and better with the artificial intelligence enabled cochlear implant speech processor. This also has the advantages of auto switching of programming with change in ambient noise, better signal to noise ratio and better 360* hearing.

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.

Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.

OBJECTIVE: To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. METHODS: This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)'s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. RESULTS: The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model's ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. CONCLUSION: We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

New dimensions in alveolar fracture treatment: Open reduction and internal fixation by minimally invasive approach combined with computer-assisted surgery.

Alveolar fractures are a common type of maxillofacial trauma, and the conventional treatment involves closed reduction and dental splinting fixation. However, closed treatment is not suitable for some complex segmental alveolar fractures. In this case report, we introduce an innovative method for segmental alveolar fracture by using open reduction and internal fixation by minimally invasive approach combined with computer-assisted surgery. In this case, the new dimensions in the treatment followed AO principles of fracture management, achieving anatomical reduction of the fracture, absolute stability of the fracture ends, proper preservation of vascular supply to soft tissues and bone, and promoting recovery through early postoperative functional training. This case provides new insights into the treatment of the complex segmental alveolar fractures with tenuous vascular supply and cannot be treated by conventional splinting fixation.

Nature of storage iron turnover.

Despite recent advance in the study of the nature of storage iron turnover, a comprehensive analysis remains lacking. This study aimed to clarify the nature of storage iron turnover. Ferritin-hemosiderin iron transformation rate and the standard normal storage iron turnover rate were utilized in this study to describe the mechanism of iron absorption in relation to ferritin and hemosiderin iron turnover. The synchronization of radioiron uptake peaks by bone marrow and liver indicates that the distribution of radioiron is proportional to the pre-existing iron levels in organs at 24 h after radioiron injection. Moreover, the synchronization indicates the independence of iron mass from red cell precursors in acquiring plasma iron. Thus, the erythron does not dominate the radioiron uptake process. The inverse correlation between transformation rate and the amount of pre-existing iron storage implies that the intra-storage iron turnover is active in iron deficiency, but inactive in iron overload. The decreased ferritin/hemosiderin iron ratio in chronic hepatitis C (CHC) with normal iron storage suggests a trend of iron transformation from ferritin into hemosiderin. The correlation between the pretreatment iron storage and the speed of rebound in CHC implies that the vacant iron-storing rooms in iron-removed cells have a potential to increase iron absorption. This study presents new insights into the turnover of stored iron to enhance our understanding of iron metabolism in various hematologic disorders.

A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI.

BACKGROUND: Accurately classifying primary bone tumors is crucial for guiding therapeutic decisions. The National Comprehensive Cancer Network guidelines recommend multimodal images to provide different perspectives for the comprehensive evaluation of primary bone tumors. However, in clinical practice, most patients' medical multimodal images are often incomplete. This study aimed to build a deep learning model using patients' incomplete multimodal images from X-ray, CT, and MRI alongside clinical characteristics to classify primary bone tumors as benign, intermediate, or malignant. METHODS: In this retrospective study, a total of 1305 patients with histopathologically confirmed primary bone tumors (internal dataset, n = 1043; external dataset, n = 262) were included from two centers between January 2010 and December 2022. We proposed a Primary Bone Tumor Classification Transformer Network (PBTC-TransNet) fusion model to classify primary bone tumors. Areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the model's classification performance. RESULTS: The PBTC-TransNet fusion model achieved satisfactory micro-average AUCs of 0.847 (95% CI: 0.832, 0.862) and 0.782 (95% CI: 0.749, 0.817) on the internal and external test sets. For the classification of benign, intermediate, and malignant primary bone tumors, the model respectively achieved AUCs of 0.827/0.727, 0.740/0.662, and 0.815/0.745 on the internal/external test sets. Furthermore, across all patient subgroups stratified by the distribution of imaging modalities, the PBTC-TransNet fusion model gained micro-average AUCs ranging from 0.700 to 0.909 and 0.640 to 0.847 on the internal and external test sets, respectively. The model showed the highest micro-average AUC of 0.909, accuracy of 84.3%, micro-average sensitivity of 84.3%, and micro-average specificity of 92.1% in those with only X-rays on the internal test set. On the external test set, the PBTC-TransNet fusion model gained the highest micro-average AUC of 0.847 for patients with X-ray + CT. CONCLUSIONS: We successfully developed and externally validated the transformer-based PBTC-Transnet fusion model for the effective classification of primary bone tumors. This model, rooted in incomplete multimodal images and clinical characteristics, effectively mirrors real-life clinical scenarios, thus enhancing its strong clinical practicability.

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.

Digital Otoscopy With Computer-Aided Composite Image Generation: Impact on the Correct Diagnosis, Confidence, and Time.

OBJECTIVE: This study investigated the comparative performance of ear, nose, and throat (ENT) physicians in correctly detecting ear abnormalities when reviewing digital otoscopy imaging using 3 different visualization methods, including computer-assisted composite images called "SelectStitch," single video frame "Still" images, and video clips. The study also explored clinicians' diagnostic confidence levels and the time to make a diagnosis. STUDY DESIGN: Clinician diagnostic reader study. SETTING: Online diagnostic survey of ENT physicians. METHODS: Nine ENT physicians reviewed digital otoscopy examinations from 86 ears with various diagnoses (normal, perforation, retraction, middle ear effusion, tympanosclerosis). Otoscopy examinations used artificial-intelligence (AI)-based computer-aided composite image generation from a video clip (SelectStitch), manually selected best still frame from a video clip (Still), or the entire video clip. Statistical analyses included comparisons of ability to detect correct diagnosis, confidence levels, and diagnosis times. RESULTS: The ENT physicians' ability to detect ear abnormalities (33.2%-68.7%) varied depending on the pathologies. SelectStitch and Still images were not statistically different in detecting abnormalities (P > .50), but both were different from Video (P < .01). However, the performance improvement observed with Videos came at the cost of significantly longer time to determining the diagnosis. The level of confidence in the diagnosis was positively associated with correct diagnoses, but varied by particular pathology. CONCLUSION: This study explores the potential of computer-assisted techniques like SelectStitch in enhancing otoscopic diagnoses and time-saving, which could benefit telemedicine settings. Comparable performance between computer-generated and manually selected images suggests the potential of AI algorithms for otoscopy applications.

Progress in Computer-Assisted Navigation for Total Knee Arthroplasty in Treating Knee Osteoarthritis with Extra-Articular Deformity.

Total knee arthroplasty (TKA) is a well-established treatment for end-stage knee osteoarthritis. However, in patients with concomitant extra-articular deformities, conventional TKA techniques may lead to unsatisfactory outcomes and higher complication rates. This review summarizes the application of navigated TKA for treating knee osteoarthritis with extra-articular deformities. The principles and potential benefits of computer navigation systems, including improved component alignment, soft tissue balancing, and restoration of mechanical axis, are discussed. Research studies demonstrate that navigated TKA can effectively correct deformities, relieve pain, and improve postoperative joint function and quality of life compared with conventional methods. The advantages of navigated TKA in terms of surgical precision, lower complication rates, and superior functional recovery are highlighted. Despite challenges like the learning curve and costs, navigated TKA is an increasingly indispensable tool for achieving satisfactory outcomes in TKA for knee osteoarthritis patients with extra-articular deformities.

Disruption of HSD17B12 in mouse hepatocytes leads to reduced body weight and defect in the lipid droplet expansion associated with microvesicular steatosis.

The function of hydroxysteroid dehydrogenase 12 (HSD17B12) in lipid metabolism is poorly understood. To study this further, we created mice with hepatocyte-specific knockout of HSD17B12 (LiB12cKO). From 2 months on, these mice showed significant fat accumulation in their liver. As they aged, they also had a reduced whole-body fat percentage. Interestingly, the liver fat accumulation did not result in the typical formation of large lipid droplets (LD); instead, small droplets were more prevalent. Thus, LiB12KO liver did not show increased macrovesicular steatosis with the increasing fat content, while microvesicular steatosis was the predominant feature in the liver. This indicates a failure in the LD expansion. This was associated with liver damage, presumably due to lipotoxicity. Notably, the lipidomics data did not support an essential role of HSD17B12 in fatty acid (FA) elongation. However, we did observe a decrease in the quantity of specific lipid species that contain FAs with carbon chain lengths of 18 and 20 atoms, including oleic acid. Of these, phosphatidylcholine and phosphatidylethanolamine have been shown to play a key role in LD formation, and a limited amount of these lipids could be part of the mechanism leading to the dysfunction in LD expansion. The increase in the Cidec expression further supported the deficiency in LD expansion in the LiB12cKO liver. This protein is crucial for the fusion and growth of LDs, along with the downregulation of several members of the major urinary protein family of proteins, which have recently been shown to be altered during endoplasmic reticulum stress.

The Emerging Role of Automation, Measurement Standardization, and Artificial Intelligence in Foot and Ankle Imaging: An Update.

In the past few years, advances in clinical imaging in the realm of foot and ankle have been consequential and game changing. Improvements in the hardware aspects, together with the development of computer-assisted interpretation and intervention tools, have led to a noticeable improvement in the quality of health care for foot and ankle patients. Focusing on the mainstay imaging tools, including radiographs, computed tomography scans, and ultrasound, in this review study, the authors explored the literature for reports on the new achievements in improving the quality, accuracy, accessibility, and affordability of clinical imaging in foot and ankle.