Purpose Recent advancements in machine learning (ML) allow for rapid analysis of complex image data, which supports the use of ultrasound (US)-based solutions in interventional procedures. These solutions often require large, labeled datasets that can be time-consuming to curate and subject to inter- and intra-labeler variability. This work presents a practical method for automated labeling of US images by transferring labels from 3D diagnostic images (e.g., CT or MR) using tracked US imaging to support supervised training. The approach was applied to segmenting spinal vertebrae, and the quality of the generated labels was evaluated by registering individual vertebrae from US to CT images to account for potential spinal deformation during surgery. Methods The proposed approach uses tracked US imaging to map target structures from CT volumes onto individual US frames. A dataset of spine images was created by scanning cadaveric torso specimens. Automated data cleaning methods were used to discard invalid frames, and data augmentations were applied to account for variability in image appearance. A simple U-Net model, called TernausNet, was trained for segmenting vertebrae using three labeling strategies: full vertebra (FV), posterior surface (PS), and weighted posterior surface (PSw). The labels were evaluated through vertebrae segmentation and registration of the resulting segmentations to corresponding CT structures, considering the impact of labeling strategy, calibration errors, and data cleaning. Results The proposed labeling strategies yielded improved segmentation accuracy over the direct mapping of CT labels (viz. FV), yielding a median of 5.18 [4.24, 6.66] mm RMSD for PS and 3.86 [2.87, 5.60] mm for PSw labeling. The PSw approach was particularly effective in reducing hallucination artifacts in the acoustic shadow regions below the vertebral cortex. Using the resulting segmentations, registrations were solved with 1.56 [1.30, 1.62] mm TRE for PS and 1.52 [1.32, 2.38] mm for PSw labeling. Automated data cleaning and augmentation were found to significantly enhance the accuracy of bone feature segmentation and vertebra registration. Conclusions The study presents an automated labeling method for US imaging that supports the training of ML models by mapping 3D structures onto 2D US frames. The results highlight the importance of proper probe calibration, data cleaning, and specific labeling strategies in mitigating segmentation and registration errors. The work demonstrates the potential of real-time US imaging as a tool for precise anatomical tracking in surgery.

Purpose. Neuroendovascular procedures, characterized by their minimally invasive nature and effectiveness, are increasingly used in the management of cerebrovascular diseases such as acute stroke and cerebral aneurysms. These procedures, however, rely on x-ray fluoroscopy for catheter guidance, exposing patients, surgeons, and surgical staff to ionizing radiation with associated health risks. To address this problem, this work introduces a new electromagnetic-based (EM) catheter navigation solution. Methods. A custom catheter was designed and constructed to integrate a 5-degree-of-freedom EM coil sensor at its tip. The recorded sensor position was used in: (1) estimating the tip direction to guide it through vessel bifurcations; and (2) dynamically reconstructing the catheter shape as it is advanced or retracted within the vessels. Instrument overlay on angiography CT images is enabled by registering the reconstructed shape to the extracted vessel centerlines. The accuracy of direction estimation, shape sensing, and path-based registration was evaluated in experimental studies on an anthropomorphic phantom. Results. The results demonstrated a mean deviation of 4.1 mm for catheter shape estimation. The catheter tip direction was resolved to within 3.4 degrees error to permit navigation through vessel bifurcations. Registration of reconstructed instrument poses to vessel centerlines achieved 5.1 mm TRE. The results demonstrate sufficient guidance accuracy within the main arteries, such as the femoral artery (.8.2-9.8 mm) or the abdominal aorta (.30 mm). Conclusions. This work reports a new system for catheter navigation during endovascular interventions. The proposed solution provides a new mode of image guidance that offers accurate treatment for the patient and helps reduce radiation exposure to the surgeon and the surgical staff.

Purpose. Robot-assisted orthopaedic joint reduction offers enhanced precision and control across multiple axes of motion, enabling precise realignment according to predefined plans. However, the high levels of forces encountered may induce unintended anatomical motion and flex mechanical components. To address this, this work presents an approach that uses 2D fluoroscopic imaging to verify and readjust the 3D reduction path by tracking deviations from the planned trajectory. Methods. The proposed method involves a 3D-2D registration algorithm using a pair of fluoroscopic images, along with prior models of each body in the radiographic scene. This objective is formulated to couple and constrain multiple object poses (fibula, tibia, talus, and robot end effector), and incorporate novel methods for automatic view and hyperparameter selection to improve robustness. The algorithms were refined through cadaver studies and evaluated in a preclinical trial, employing a robotic system to manipulate a dislocated fibula. Results. Studies with cadaveric specimens highlighted the joint-specific formulation’s high registration accuracy (Δx = 0.3 ± 1.5 mm), further improved with the use of automatic view and hyperparameter selection (Δx = 0.2 ± 0.8 mm). Preclinical studies demonstrated a high deviation between the intended and the actual path of the robotic system, which was accurately captured (Δx < 1 mm) using the proposed techniques. Conclusions. The solution offers to close the loop on image-based guidance of robot-assisted joint reduction by tracking the robot and bones to dynamically correct the course. The approach uses standard clinical images and is expected to lower radiation exposure by providing 3D information and allowing the staff to stay clear of the x-ray beam.

This study introduces the physical principles and safety evaluation of recombinant bovine lactoferrin (fusion factor) as an innovative biomaterial. Fusion factor is a recombinant lactoferrin expressed by fusing lactoferrin, which has natural biological defense function, with other peptide segments through sequence optimization. It is named fusion factor. Its molecular weight is about 36kDa, which is much greater than the 1kDa molecular weight limit of macromolecular transdermal absorption, so it is not absorbed when used externally on the epithelial mucosa. The lactoferrin based biological defense functional peptide segment in the fusion factor can neutralize the virus by binding to viral protein nucleic acid through the physical action of charge adsorption, and can also compete with cell receptors to inhibit virus infection in cells. The molar ratio of the transmembrane peptide (Pep-1) fragment to the carrier protein is 1:1, so only the transport protein is anchored to the cell surface, forming a physical isolation protein protective wall against viruses and bacteria, without penetrating the cell or damaging the cell membrane. The fusion factor and its derived vaginal bacteria blocking gel have no significant toxicity, sensitization, anaphylaxis or delayed hypersensitivity in vitro cell experiments, in vivo animal experiments and clinical observation tests, and have no side effects with highly safety.

Purpose. Conventional image-guided spine surgery relies on surgical trackers for real-time localization of instruments with respect to pre- or intra-operative CT images. These solutions, however, are susceptible to anatomical deformations that may occur due to patient repositioning or imparted changes during surgery. This work presents an approach that uses intraoperative tracked ultrasound (US) imaging to provide real-time verification and recovery of surgical tracking accuracy following spinal deformations. Methods. The approach combines deep-learning segmentation of the posterior vertebral cortices with a multi-step point-to-surface registration that maps reconstructed US features to the 3D CT image. The method was trained on co-registered CT and US images from 5 cadaveric specimens and validated on 2 separate specimens. The geometric accuracy of the registrations was quantified over target regions covering potential pedicle screw entry points. Results. The study confirmed the optimal level for the confidence threshold of the network output and evaluated the minimum required scan length. Vertebrae with simulated displacements were registered with 1.7 +/- 0.3 mm of error. The results were robust for up to 50 mm of initial displacement. Conclusions. The solution offers a fast (real-time), portable (small device footprint), and safe (no ionizing radiation) method of tracking anatomical change during surgery. Work currently underway includes implementation of a prototype system for real-time use and evaluation of the surgical workflow with respect to factors including acquisition time, scan extent (number of vertebrae), and scan planes/trajectories.

Background: Alzheimer's disease (AD) is the most common neurodegenerative disease characterized with mainly cognitive impairments, and the number of elderly having AD is continuously increasing.Objective: To summarize the pathogenesis of AD and the therapeutic effects of flavonoids on related inflammatory processes.Main ideas: Flavonoids have been shown to alleviate effects of AD both in vitro and in vivo.Conclusion: The clinical significance of these research summaries lay a potential groundwork for the development of new drugs targeting AD treatment.

Although deep learning (DL) algorithms have demonstrated promising performance in radiology, they can exhibit bias related to lack of diversity in datasets used for algorithm development. Convolutional neural networks (CNN) trained to classify disease on chest radiographs (CXR) on single-sex images have been shown to underperform on the opposite sex, presumably related to sex-specific anatomy, like breast tissue. Whether these biases exist for body parts without sexbased anatomical differences that are as obvious, such as the bones, however, is unclear. We evaluated if sex imbalance in training data results in biased DL models for identification of knee osteoarthritis.

Cardiac tagged Magnetic Resonance Imaging (MRI) is readily available in clinical and preclinical scanners and can be used to quantitatively characterize cardiac motion. Because tag lines fade towards the end of cardiac cycle, accurately extracting their location over the full cardiac cycle is particularly challenging. In this work, we present a geometric flow approach to the segmentation of tag lines in cardiac MRI. This algorithm is based on the diffeomorphic active contour algorithm that evolves straight lines placed on the tagged MR images to match taglines. We evaluated the accuracy of the algorithm using the Hausdorff distance, with errors of 1.62 (± 0.21 SD) pixel units for horizontal tag lines and 1.79 (± 0.62 SD) pixel units for vertical ones generated from long axis cardiac images. Our algorithm performed well in the presence of noise or large deformations.

OBJECTIVE:Periprosthetic dislocations of total hip arthroplasty (THA) are time-sensitive injuries, as the longer diagnosis and treatment are delayed, the more difficult they are to reduce. Automated triage of radiographs with dislocations could help reduce these delays. We trained convolutional neural networks (CNNs) for the detection of THA dislocations, and evaluated their generalizability by evaluating them on external datasets.METHODS:We used 357 THA radiographs from a single hospital (185 with dislocation [51.8%]) to develop and internally test a variety of CNNs to identify THA dislocation. We performed external testing of these CNNs on two datasets to evaluate generalizability. CNN performance was evaluated using area under the receiving operating characteristic curve (AUROC). Class activation mapping (CAM) was used to create heatmaps of test images for visualization of regions emphasized by the CNNs.RESULTS:Multiple CNNs achieved AUCs of 1 for both internal and external test sets, indicating good generalizability. Heatmaps showed that CNNs consistently emphasized the THA for both dislocated and located THAs.CONCLUSION:CNNs can be trained to recognize THA dislocation with high diagnostic performance, which supports their potential use for triage in the emergency department. Importantly, our CNNs generalized well to external data from two sources, further supporting their potential clinical utility.

OBJECTIVE:Deep learning has the potential to automatically triage orthopedic emergencies, such as joint dislocations. However, due to the rarity of these injuries, collecting large numbers of images to train algorithms may be infeasible for many centers. We evaluated if the Internet could be used as a source of images to train convolutional neural networks (CNNs) for joint dislocations that would generalize well to real-world clinical cases.METHODS:We collected datasets from online radiology repositories of 100 radiographs each (50 dislocated, 50 located) for four joints: native shoulder, elbow, hip, and total hip arthroplasty (THA). We trained a variety of CNN binary classifiers using both on-the-fly and static data augmentation to identify the various joint dislocations. The best-performing classifier for each joint was evaluated on an external test set of 100 corresponding radiographs (50 dislocations) from three hospitals. CNN performance was evaluated using area under the ROC curve (AUROC). To determine areas emphasized by the CNN for decision-making, class activation map (CAM) heatmaps were generated for test images.RESULTS:The best-performing CNNs for elbow, hip, shoulder, and THA dislocation achieved high AUROCs on both internal and external test sets (internal/external AUC): elbow (1.0/0.998), hip (0.993/0.880), shoulder (1.0/0.993), THA (1.0/0.950). Heatmaps demonstrated appropriate emphasis of joints for both located and dislocated joints.CONCLUSION:With modest numbers of images, radiographs from the Internet can be used to train clinically-generalizable CNNs for joint dislocations. Given the rarity of joint dislocations at many centers, online repositories may be a viable source for CNN-training data.

Objective: Joint dislocations are orthopedic emergencies that require prompt intervention. Automatic identifi-cation of these injuries could help improve timely patient care because diagnostic delays increase the difficulty of reduction. In this study, we developed convolutional neural networks (CNNs) to detect elbow and shoulder dislocations, and tested their generalizability on external datasets.Methods: We collected 106 elbow radiographs (53 with dislocation [50 %]) and 140 shoulder radiographs (70 with dislocation [50 %]) from a level-1 trauma center. After performing 24x data augmentation on training/ validation data, we trained multiple CNNs to detect elbow and shoulder dislocations, and also evaluated the best -performing models using external datasets from an external hospital and online radiology repositories. To examine CNN decision-making, we generated class activation maps (CAMs) to visualize areas of images that contributed the most to model decisions.Results: On all internal test sets, CNNs achieved AUCs >0.99, and on all external test sets, CNNs achieved AUCs >0.97. CAMs demonstrated that the CNNs were focused on relevant joints in decision-making regardless of whether or not dislocations were present.Conclusion: Joint dislocations in both shoulders and elbows were readily identified with high accuracy by CNNs with excellent generalizability to external test sets. These findings suggest that CNNs could expedite access to intervention by assisting in diagnosing dislocations.

Small rodent cardiac magnetic resonance imaging (MRI) plays an important role in preclinical models of cardiac disease. Accurate myocardial boundaries delineation is crucial to most morphological and functional analysis in rodent cardiac MRIs. However, rodent cardiac MRIs, due to animal’s small cardiac volume and high heart rate, are usually acquired with sub-optimal resolution and low signal-to-noise ratio (SNR). These rodent cardiac MRIs can also suffer from signal loss due to the intra-voxel dephasing. These factors make automatic myocardial segmentation challenging. Manual contouring could be applied to label myocardial boundaries but it is usually laborious, time consuming, and not systematically objective. In this study, we present a deep learning approach based on 3D attention M-net to perform automatic segmentation of left ventricular myocardium. In the deep learning architecture, we use dual spatial-channel attention gates between encoder and decoder along with multi-scale feature fusion path after decoder. Attention gates enable networks to focus on relevant spatial information and channel features to improve segmentation performance. A distance derived loss term, besides general dice loss and binary cross entropy loss, was also introduced to our hybrid loss functions to refine segmentation contours. The proposed model outperforms other generic models, like U-Net and FCN, in major segmentation metrics including the dice score (0.9072), Jaccard index (0.8307) and Hausdorff distance (3.1754 pixels), which are comparable to the results achieved by state-of-the-art models on human cardiac ACDC17 datasets.Clinical relevance Small rodent cardiac MRI is routinely used to probe the effect of individual genes or groups of genes on the etiology of a large number of cardiovascular diseases. An automatic myocardium segmentation algorithm specifically designed for these data can enhance accuracy and reproducibility of cardiac structure and function analysis.

To develop and test the performance of deep convolutional neural networks (DCNNs) for automated classification of age and sex on chest radiographs (CXR). We obtained 112,120 frontal CXRs from the NIH ChestX-ray14 database performed in 48,780 females (44%) and 63,340 males (56%) ranging from 1 to 95 years old. The dataset was split into training (70%), validation (10%), and test (20%) datasets, and used to fine-tune ResNet-18 DCNNs pretrained on ImageNet for (1) determination of sex (using entire dataset and only pediatric CXRs); (2) determination of age < 18 years old or ≥ 18 years old (using entire dataset); and (3) determination of age < 11 years old or 11–18 years old (using only pediatric CXRs). External testing was performed on 662 CXRs from China. Area under the receiver operating characteristic curve (AUC) was used to evaluate DCNN test performance. DCNNs trained to determine sex on the entire dataset and pediatric CXRs only had AUCs of 1.0 and 0.91, respectively (p < 0.0001). DCNNs trained to determine age < or ≥ 18 years old and < 11 vs. 11–18 years old had AUCs of 0.99 and 0.96 (p < 0.0001), respectively. External testing showed AUC of 0.98 for sex (p = 0.01) and 0.91 for determining age < or ≥ 18 years old (p < 0.001). DCNNs can accurately predict sex from CXRs and distinguish between adult and pediatric patients in both American and Chinese populations. The ability to glean demographic information from CXRs may aid forensic investigations, as well as help identify novel anatomic landmarks for sex and age.

BACKGROUND:To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies. METHODS:Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation. RESULTS:During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98-0.99), sensitivity 94% (95 CI: 91%-97%), and specificity 96% (95 CI: 93%-99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%. CONCLUSION:In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future.

As the coronavirus disease 2019 (COVID-19) becomes a global pandemic, policy makers must enact interventions to stop its spread. Data driven approaches might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and in come, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph. By collecting these data, as well as providing tools to read them, we hope to aid researchers investigating how the disease spreads and which communities are best able to accommodate stay-at-home mitigation efforts. Our dataset and associated code are available at https://github.com/JieYingWu/COVID-19_US_County-level_Summaries.

Objective To develop and evaluate the performance of deep convolutional neural networks (DCNN) to detect and identify specific total shoulder arthroplasty (TSA) models. Materials and methods We included 482 radiography studies obtained from publicly available image repositories with native shoulders, reverse TSA (RTSA) implants, and five different TSA models. We trained separate ResNet DCNN-based binary classifiers to (1) detect the presence of shoulder arthroplasty implants, (2) differentiate between TSA and RTSA, and (3) differentiate between the five TSA models, using five individual classifiers for each model, respectively. Datasets were divided into training, validation, and test datasets. Training and validation datasets were 20-fold augmented. Test performances were assessed with area under the receiver-operating characteristic curves (AUC-ROC) analyses. Class activation mapping was used to identify distinguishing imaging features used for DCNN classification decisions. Results The DCNN for the detection of the presence of shoulder arthroplasty implants achieved an AUC-ROC of 1.0, whereas the AUC-ROC for differentiation between TSA and RTSA was 0.97. Class activation map analysis demonstrated the emphasis on the characteristic arthroplasty components in decision-making. DCNNs trained to distinguish between the five TSA models achieved AUC-ROCs ranging from 0.86 for Stryker Solar to 1.0 for Zimmer Bigliani-Flatow with class activation map analysis demonstrating an emphasis on unique implant design features. Conclusion DCNNs can accurately identify the presence of and distinguish between TSA & RTSA, and classify five specific TSA models with high accuracy. The proof of concept of these DCNNs may set the foundation for an automated arthroplasty atlas for rapid and comprehensive model identification.

Background: Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. Methods: Using transfer learning, we modified the ResNet-152 deep convolutional neural network (DCNN), pretrained on ImageNet, to determine the optic disc laterality. After a 5-fold cross-validation, we generated receiver operating characteristic curves and corresponding area under the curve (AUC) values to evaluate performance. The data set consisted of 576 color fundus photographs (51% right and 49% left). Both 30° photographs centered on the optic disc (63%) and photographs with varying degree of optic disc centration and/or wider field of view (37%) were included. Both normal (27%) and abnormal (73%) optic discs were included. Various neuro-ophthalmological diseases were represented, such as, but not limited to, atrophy, anterior ischemic optic neuropathy, hypoplasia, and papilledema. Results: Using 5-fold cross-validation (70% training; 10% validation; 20% testing), our DCNN for classifying right vs left optic disc achieved an average AUC of 0.999 (±0.002) with optimal threshold values, yielding an average accuracy of 98.78% (±1.52%), sensitivity of 98.60% (±1.72%), and specificity of 98.97% (±1.38%). When tested against a separate data set for external validation, our 5-fold cross-validation model achieved the following average performance: AUC 0.996 (±0.005), accuracy 97.2% (±2.0%), sensitivity 96.4% (±4.3%), and specificity 98.0% (±2.2%). Conclusions: Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.

As the coronavirus disease 2019 (COVID-19) continues to be a global pandemic, policy makers have enacted and reversed non-pharmaceutical interventions with various levels of restrictions to limit its spread. Data driven approaches that analyze temporal characteristics of the pandemic and its dependence on regional conditions might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction on the example of the United States, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph and Google mobility reports. We compile information from IHME, state and county-level government, and newspapers for dates of the enactment and reversal of non-pharmaceutical interventions. By collecting these data, as well as providing tools to read them, we hope to accelerate research that investigates how the disease spreads and why spread may be different across regions. Our dataset and associated code are available at this http URL.