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Radiology Radiology

Advances in micro-CT imaging of small animals.

In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PURPOSE : Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research.

METHODS : Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field.

RESULTS : Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT.

CONCLUSIONS : All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

Clark D P, Badea C T


Contrast agents, Deep learning, Micro-CT, Nanoparticles, Phase contrast, Photon counting detector, Preclinical, Spectral CT, Theranostics

General General

Changes in alcohol use during the COVID-19 pandemic among American veterans.

In Addictive behaviors ; h5-index 60.0

BACKGROUND : The COVID-19 pandemic has had considerable behavioral health implications globally. One subgroup that may be of particular concern is U.S. veterans, who are susceptible to mental health and substance use concerns. The current study aimed to investigate changes in alcohol use and binge drinking before and during the first year of the pandemic among U.S. veterans, and how pre-pandemic mental health disorders, namely posttraumatic stress disorder (PTSD), and COVID-19-related factors like loneliness, negative reactions to COVID-19, and economic hardship influenced alcohol use trends.

METHODS : 1230 veterans were recruited in February 2020 as part of a larger survey study on veteran health behaviors. Veterans were asked to complete follow-up assessments throughout the pandemic at 6, 9, and 12- months.

RESULTS : Overall, veterans reported a significant decrease in alcohol use (IRR = 0.98) and binge drinking (IRR = 0.11) However, women, racial/ethnic minority veterans, and those with pre-existing PTSD exhibited smaller decreases in alcohol use and binge drinking and overall higher rates of use compared to men, White veterans, and those without PTSD. Both economic hardship and negative reactions to COVID-19 were associated with greater alcohol and binge drinking whereas loneliness showed a negative association with alcohol use and binge drinking.

CONCLUSIONS : Veterans reported decreases in alcohol use and binge drinking throughout the pandemic, with heterogeneity in these outcomes noted for higher risk groups. Special research and clinical attention should be given to the behavioral health care needs of veterans in the post-pandemic period.

Davis Jordan P, Prindle John, Castro Carl C, Saba Shaddy, Fitzke Reagan E, Pedersen Eric R


Active duty, COVID-19, Drug use, Longitudinal, Trauma, Veterans administration

Ophthalmology Ophthalmology

Can artificial intelligence predict glaucomatous visual field progression?: A spatial-ordinal convolutional neural network model.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : To develop an artificial neural network model incorporating both spatial and ordinal approaches to predict glaucomatous visual field (VF) progression.

DESIGN : Cohort study.

PARTICIPANTS : From a cohort of primary open-angle glaucoma patients, 9,212 eyes of 6,047 patients who underwent regular reliable VF examinations for >4 years were included.

METHODS : We constructed all possible spatial-ordinal tensors by stacking three consecutive VF tests (VF-blocks) with at least 3 years of follow-up. Trend-based, event-based and combined criteria were defined to determine the progression. VF-blocks were considered "progressed" if progression occurred within 3 years; the progression was further confirmed after 3 years. We constructed six convolutional neural network (NN) models and two linear models: regression on global indices and pointwise linear regression (PLR). We compared area under the receiver operating characteristic curve (AUROC) of each models for the prediction of glaucomatous VF progression.

RESULTS : Among 43,260 VF-blocks, 4,406 (10.2%), 4,376 (10.1%), and 2,394 (5.5%) VF blocks were classified as progression based on trend-based, event-based and combined criteria. For all three criteria, the progression group was significantly older and had worse initial MD and VFI than the non-progression group (p < 0.001 for all). The best-performing NN model had an AUROC of 0.864 with sensitivity of 0.42 at specificity of 0.95. In contrast, an AUROC of 0.611 was estimated from sensitivity of 0.28 at specificity of 0.84 for the PLR.

CONCLUSIONS : The NN models incorporating spatial-ordinal characteristics demonstrated significantly better performance than the linear models in the prediction of glaucomatous VF progression.

Shon Kilhwan, Sung Kyung Rim, Shin Joong Won


artificial intelligence, glaucoma, machine learning, visual field

General General

Improve automatic detection of animal call sequences with temporal context.

In Journal of the Royal Society, Interface

Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (Balaenoptera physalus) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9-17% increase in area under the precision-recall curve and a 9-18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.

Madhusudhana Shyam, Shiu Yu, Klinck Holger, Fleishman Erica, Liu Xiaobai, Nosal Eva-Marie, Helble Tyler, Cholewiak Danielle, Gillespie Douglas, Širović Ana, Roch Marie A


bioacoustics, improved performance, machine learning, passive acoustic monitoring, robust automatic recognition, temporal context

Radiology Radiology

Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.

In PloS one ; h5-index 176.0

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

Lee Ari, Kim Min Su, Han Sang-Sun, Park PooGyeon, Lee Chena, Yun Jong Pil


General General

Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources.

In PLoS computational biology

The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in reasonable time. To speed up applications and utilize available compute resources as efficient as possible, software developers make use of parallelization mechanisms, like multithreading. Many of the available tools in bioinformatics offer multithreading capabilities, but more compute power is not always helpful. In this study we investigated the behavior of well-known applications in bioinformatics, regarding their performance in the terms of scaling, different virtual environments and different datasets with our benchmarking tool suite BOOTABLE. The tool suite includes the tools BBMap, Bowtie2, BWA, Velvet, IDBA, SPAdes, Clustal Omega, MAFFT, SINA and GROMACS. In addition we added an application using the machine learning framework TensorFlow. Machine learning is not directly part of bioinformatics but applied to many biological problems, especially in the context of medical images (X-ray photographs). The mentioned tools have been analyzed in two different virtual environments, a virtual machine environment based on the OpenStack cloud software and in a Docker environment. The gained performance values were compared to a bare-metal setup and among each other. The study reveals, that the used virtual environments produce an overhead in the range of seven to twenty-five percent compared to the bare-metal environment. The scaling measurements showed, that some of the analyzed tools do not benefit from using larger amounts of computing resources, whereas others showed an almost linear scaling behavior. The findings of this study have been generalized as far as possible and should help users to find the best amount of resources for their analysis. Further, the results provide valuable information for resource providers to handle their resources as efficiently as possible and raise the user community's awareness of the efficient usage of computing resources.

Hanussek Maximilian, Bartusch Felix, Krüger Jens