Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients.

Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts. Conclusions: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence.

The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts large and diverse publicly available cancer image data collections. By harmonizing all data based on industry standards and colocalizing it with analysis and exploration resources, the IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. Published under a CC BY 4.0 license.

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases.

Background: The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (p < 0.05) No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves of the lungs and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by radiological experts. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology.

The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.

Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.

Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom .

Implementing the DICOM Standard for Digital Pathology.

BACKGROUND: Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. METHODS: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. RESULTS: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. CONCLUSION: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.

Light-Nuclei Spectra from Chiral Dynamics.

In recent years local chiral interactions have been derived and implemented in quantum Monte Carlo methods in order to test to what extent the chiral effective field theory framework impacts our knowledge of few- and many-body systems. In this Letter, we present Green's function Monte Carlo calculations of light nuclei based on the family of local two-body interactions presented by our group in a previous paper in conjunction with chiral three-body interactions fitted to bound- and scattering-state observables in the three-nucleon sector. These interactions include Δ intermediate states in their two-pion-exchange components. We obtain predictions for the energy levels and level ordering of nuclei in the mass range A=4-12, accurate to ≤2% of the binding energy, in very satisfactory agreement with experimental data.

Electromagnetic Response of

The longitudinal and transverse electromagnetic response functions of ^{12}C are computed in a "first-principles" Green's function Monte Carlo calculation, based on realistic two- and three-nucleon interactions and associated one- and two-body currents. We find excellent agreement between theory and experiment and, in particular, no evidence for the quenching of the measured versus calculated longitudinal response. This is further corroborated by a reanalysis of the Coulomb sum rule, in which the contributions from the low-lying J^{π}=2^{+}, 0_{2}^{+} (Hoyle), and 4^{+} states in ^{12}C are accounted for explicitly in evaluating the total inelastic strength.

High throughput tools to access images from clinical archives for research.

Historically, medical images collected in the course of clinical care have been difficult to access for secondary research studies. While there is a tremendous potential value in the large volume of studies contained in clinical image archives, Picture Archiving and Communication Systems (PACS) are designed to optimize clinical operations and workflow. Search capabilities in PACS are basic, limiting their use for population studies, and duplication of archives for research is costly. To address this need, we augment the Informatics for Integrating Biology and the Bedside (i2b2) open source software, providing investigators with the tools necessary to query and integrate medical record and clinical research data. Over 100 healthcare institutions have installed this suite of software tools that allows investigators to search medical record metadata including images for specific types of patients. In this report, we describe a new Medical Imaging Informatics Bench to Bedside (mi2b2) module ( www.mi2b2.org ), available now as an open source addition to the i2b2 software platform that allows medical imaging examinations collected during routine clinical care to be made available to translational investigators directly from their institution's clinical PACS for research and educational use in compliance with the Health Insurance Portability and Accountability Act (HIPAA) Omnibus Rule. Access governance within the mi2b2 module is customizable per institution and PACS minimizing impact on clinical systems. Currently in active use at our institutions, this new technology has already been used to facilitate access to thousands of clinical MRI brain studies representing specific patient phenotypes for use in research.

Neutral weak current two-body contributions in inclusive scattering from 12C.

An ab initio calculation of the sum rules of the neutral weak response functions in 12C is reported, based on a realistic Hamiltonian, including two- and three-nucleon potentials, and on realistic currents, consisting of one- and two-body terms. We find that the sum rules of the response functions associated with the longitudinal and transverse components of the (spacelike) neutral current are largest and that a significant portion (≃30%) of the calculated strength is due to two-body terms. This fact may have implications for the MiniBooNE and other neutrino quasielastic scattering data on nuclei.

Charge form factor and sum rules of electromagnetic response functions in 12C.

An ab initio calculation of the 12C elastic form factor and sum rules of longitudinal and transverse response functions measured in inclusive (e, e') scattering are reported, based on realistic nuclear potentials and electromagnetic currents. The longitudinal elastic form factor and sum rule are found to be in satisfactory agreement with available experimental data. A direct comparison between theory and experiment is difficult for the transverse sum rule. However, it is shown that the calculated transverse sum rule has large contributions from two-body currents, indicating that these mechanisms lead to a significant enhancement of the quasielastic transverse response. This fact may have implications for the anomaly observed in recent neutrino quasielastic charge-changing scattering experiments on 12C.

Surgery, virtual reality, and the future.

MMVR has provided the leading forum for the multidisciplinary interaction and development of the use of Virtual Reality (VR) techniques in medicine, particularly in surgical practice. Here we look back at the foundations of our field, focusing on the use of VR in Surgery and similar interventional procedures, sum up the current status, and describe the challenges and opportunities going forward.

Differences in simple morphological variables in ruptured and unruptured middle cerebral artery aneurysms.

OBJECT: Management of unruptured intracranial aneurysms remains controversial in neurosurgery. The contribution of morphological parameters has not been included in the treatment paradigm in a systematic manner or for any particular aneurysm location. The authors present a large sample of middle cerebral artery (MCA) aneurysms that were assessed using morphological variables to determine the parameters associated with aneurysm rupture. METHODS: Preoperative CT angiography (CTA) studies were evaluated using Slicer software to generate 3D models of the aneurysms and their surrounding vascular architecture. Morphological parameters examined in each model included 5 variables already defined in the literature (aneurysm size, aspect ratio, aneurysm angle, vessel angle, and size ratio) and 3 novel variables (flow angle, distance to the genu, and parent-daughter angle). Univariate and multivariate statistical analyses were performed to determine statistical significance. RESULTS: Between 2005 and 2008, 132 MCA aneurysms were treated at a single institution, and CTA studies of 79 aneurysms (40 ruptured and 39 unruptured) were analyzed. Fifty-three aneurysms were excluded because of reoperation (4), associated AVM (2), or lack of preoperative CTA studies (47). Ruptured aneurysms were associated with larger size, greater aspect ratio, larger aneurysm and flow angles, and smaller parent-daughter angle. Multivariate logistic regression revealed that aspect ratio, flow angle, and parent-daughter angle were the strongest factors associated with ruptured aneurysms. CONCLUSIONS: Aspect ratio, flow angle, and parent-daughter angle are more strongly associated with ruptured MCA aneurysms than size. The association of parameters independent of aneurysm morphology with ruptured aneurysms suggests that these parameters may be associated with an increased risk of aneurysm rupture. These factors are readily applied in clinical practice and should be considered in addition to aneurysm size when assessing the risk of aneurysm rupture specific to the MCA location.

Cold neutrons trapped in external fields.

The properties of inhomogeneous neutron matter are crucial to the physics of neutron-rich nuclei and the crust of neutron stars. Advances in computational techniques now allow us to accurately determine the binding energies and densities of many neutrons interacting via realistic microscopic interactions and confined in external fields. We perform calculations for different external fields and across several shells to place important constraints on inhomogeneous neutron matter, and hence the large isospin limit of the nuclear energy density functionals that are used to predict properties of heavy nuclei and neutron star crusts. We find important differences between microscopic calculations and current density functionals; in particular, the isovector gradient terms are significantly more repulsive than in traditional models, and the spin-orbit and pairing forces are comparatively weaker.

Interactive diffusion tensor tractography visualization for neurosurgical planning.

BACKGROUND: Diffusion tensor imaging (DTI) infers the trajectory and location of large white matter tracts by measuring the anisotropic diffusion of water. DTI data may then be analyzed and presented as tractography for visualization of the tracts in 3 dimensions. Despite the important information contained in tractography images, usefulness for neurosurgical planning has been limited by the inability to define which are critical structures within the mass of demonstrated fibers and to clarify their relationship to the tumor. OBJECTIVE: To develop a method to allow the interactive querying of tractography data sets for surgical planning and to provide a working software package for the research community. METHODS: The tool was implemented within an open source software project. Echo-planar DTI at 3 T was performed on 5 patients, followed by tensor calculation. Software was developed that allowed the placement of a dynamic seed point for local selection of fibers and for fiber display around a segmented structure, both with tunable parameters. A neurosurgeon was trained in the use of software in < 1 hour and used it to review cases. RESULTS: Tracts near tumor and critical structures were interactively visualized in 3 dimensions to determine spatial relationships to lesion. Tracts were selected using 3 methods: anatomical and functional magnetic resonance imaging-defined regions of interest, distance from the segmented tumor volume, and dynamic seed-point spheres. CONCLUSION: Interactive tractography successfully enabled inspection of white matter structures that were in proximity to lesions, critical structures, and functional cortical areas, allowing the surgeon to explore the relationships between them.

Precise electromagnetic tests of ab initio calculations of light nuclei: states in 10Be.

In order to test ab initio calculations of light nuclei, we have remeasured lifetimes in 10Be using the Doppler shift attenuation method (DSAM) following the 7Li(7Li,alpha)10Be reaction at 8 and 10 MeV. The new experiments significantly reduce systematic uncertainties in the DSAM technique. The J(pi) = 2(1)(+) state at 3.37 MeV has tau = 205 +/- (5)(stat) +/- (7)(sys) fs corresponding to a B(E2 down) of 9.2(3)e(2) fm(4) in broad agreement with many calculations. The J(pi) = 2(2)(+) state at 5.96 MeV was found to have a B(E2 down) of 0.11(2)e(2) fm(4) and provides a more discriminating test of nuclear models. New Green's function Monte Carlo calculations for these states and transitions with a number of Hamiltonians are also reported and compared to experiment.

Quantum Monte Carlo calculations of neutron-alpha scattering.

We describe a new method to treat low-energy scattering problems in few-nucleon systems, and we apply it to the five-body case of neutron-alpha scattering. The method allows precise calculations of low-lying resonances and their widths. We find that a good three-nucleon interaction is crucial to obtain an accurate description of neutron-alpha scattering.

Tensor forces and the ground-state structure of nuclei.

Two-nucleon momentum distributions are calculated for the ground states of nuclei with mass number A< or =8, using variational Monte Carlo wave functions derived from a realistic Hamiltonian with two- and three-nucleon potentials. The momentum distribution of np pairs is found to be much larger than that of pp pairs for values of the relative momentum in the range (300-600) MeV/c and vanishing total momentum. This order of magnitude difference is seen in all nuclei considered and has a universal character originating from the tensor components present in any realistic nucleon-nucleon potential. The correlations induced by the tensor force strongly influence the structure of np pairs, which are predominantly in deuteronlike states, while they are ineffective for pp pairs, which are mostly in 1S0 states. These features should be easily observable in two-nucleon knockout processes, such as A(e,e'np) and A(e,e'pp).

Reproducibility of functional MR imaging: preliminary results of prospective multi-institutional study performed by Biomedical Informatics Research Network.

PURPOSE: To prospectively investigate the factors--including subject, brain hemisphere, study site, field strength, imaging unit vendor, imaging run, and examination visit--affecting the reproducibility of functional magnetic resonance (MR) imaging activations based on a repeated sensory-motor (SM) task. MATERIALS AND METHODS: The institutional review boards of all participating sites approved this HIPAA-compliant study. All subjects gave informed consent. Functional MR imaging data were repeatedly acquired from five healthy men aged 20-29 years who performed the same SM task at 10 sites. Five 1.5-T MR imaging units, four 3.0-T units, and one 4.0-T unit were used. The subjects performed bilateral finger tapping on button boxes with a 3-Hz audio cue and a reversing checkerboard. In a block design, 15-second epochs of alternating baseline and tasks yielded 85 acquisitions per run. Functional MR images were acquired with block-design echo-planar or spiral gradient-echo sequences. Brain activation maps standardized in a unit-sphere for the left and right hemispheres of each subject were constructed. Areas under the receiver operating characteristic curve, intraclass correlation coefficients, multiple regression analysis, and paired Student t tests were used for statistical analyses. RESULTS: Significant factors were subject (P < .005), k-space (P < .005), and field strength (P = .02) for sensitivity and subject (P = .03) and k-space (P = .05) for specificity. At 1.5-T MR imaging, mean sensitivities ranged from 7% to 32% and mean specificities were higher than 99%. At 3.0 T, mean sensitivities and specificities ranged from 42% to 85% and from 96% to 99%, respectively. At 4.0 T, mean sensitivities and specificities ranged from 41% to 73% and from 95% to 99%, respectively. Mean areas under the receiver operating characteristic curve (+/- their standard errors) were 0.77 +/- 0.05 at 1.5 T, 0.90 +/- 0.09 at 3.0 T, and 0.95 +/- 0.02 at 4.0 T, with significant differences between the 1.5- and 3.0-T examinations and between the 1.5- and 4.0-T examinations (P < .01 for both comparisons). Intraclass correlation coefficients ranged from 0.49 to 0.71. CONCLUSION: MR imaging at 3.0- and 4.0-T yielded higher reproducibility across sites and significantly better results than 1.5-T imaging. The effects of subject, k-space, and field strength on examination reproducibility were significant.

Neutron spectroscopic factors in 9Li from 2H(8Li,p)9Li.

We have studied the 2H(8Li,p)9Li reaction to obtain information on the spins, parities, and single-neutron spectroscopic factors for states in 9Li, using a radioactive 8Li beam. The deduced properties of the lowest three states are compared to the predictions of a number of calculations for the structure of 9Li. The results of ab initio quantum Monte Carlo calculations are in good agreement with the observed properties.