Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies.

HYPOTHESIS: This study investigates the impact of different diffusion magnetic imaging (dMRI) acquisition settings and mathematical fiber models on tractography performance for depicting cranial nerve (CN) VII in healthy young adults. BACKGROUND: The aim of this study is to optimize visualization of CN VII for preoperative assessment in surgeries near the nerve in the cerebellopontine angle, reducing surgery-associated complications. The study analyzes 100 CN VII in dMRI images from the Human Connectome Project, using three separate sets with different b values ( b = 1,000 s/mm 2 , b =2,000 s/mm 2 , b =3,000 s/mm 2 ) and four different tractography methods, resulting in 1,200 tractographies analyzed. RESULTS: The results show that multifiber and free water (FW) compartment models produce significantly more streamlines than single-fiber tractography. The addition of an FW compartment significantly increases the mean streamline fractional anisotropy (FA). Expert quality ratings showed that the highest rated tractography was the 1 tensor (1T) method without FW at b values of 1,000 s/mm2. CONCLUSIONS: In this young and healthy cohort, best tractography results are obtained by using a 1T model without a FW compartment in b =1,000 diffusion MR images. The FW compartment increased the contrast between streamlines and cerebrospinal fluid (higher mean streamline FA). This finding may help ongoing research to improve CN VII tractography results in tumor cases where the nerve is often stretched and thinned by the tumor.

End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

An end-to-end geometry-based pipeline for automatic preoperative surgical planning of pelvic fracture reduction and fixation.

Computer-assisted preoperative planning of pelvic fracture reduction surgery has the potential to increase the accuracy of the surgery and to reduce complications. However, the diversity of the pelvic fractures and the disturbance of small fracture fragments present a great challenge to perform reliable automatic preoperative planning. In this paper, we present a comprehensive and automatic preoperative planning pipeline for pelvic fracture surgery. It includes pelvic fracture labeling, reduction planning of the fracture, and customized screw implantation. First, automatic bone fracture labeling is performed based on the separation of the fracture sections. Then, fracture reduction planning is performed based on automatic extraction and pairing of the fracture surfaces. Finally, screw implantation is planned using the adjoint fracture surfaces. The proposed pipeline was tested on different types of pelvic fracture in 14 clinical cases. Our method achieved a translational and rotational accuracy of 2.56 mm and 3.31° in reduction planning. For fixation planning, a clinical acceptance rate of 86.7% was achieved. The results demonstrate the feasibility of the clinical application of our method. Our method has shown accuracy and reliability for complex multi-body bone fractures, which may provide effective clinical preoperative guidance and may improve the accuracy of pelvic fracture reduction surgery.

Cloud-based large-scale curation of medical imaging data using AI segmentation.

Rapid advances in medical imaging Artificial Intelligence (AI) offer unprecedented opportunities for automatic analysis and extraction of data from large imaging collections. Computational demands of such modern AI tools may be difficult to satisfy with the capabilities available on premises. Cloud computing offers the promise of economical access and extreme scalability. Few studies examine the price/performance tradeoffs of using the cloud, in particular for medical image analysis tasks. We investigate the use of cloud-provisioned compute resources for AI-based curation of the National Lung Screening Trial (NLST) Computed Tomography (CT) images available from the National Cancer Institute (NCI) Imaging Data Commons (IDC). We evaluated NCI Cancer Research Data Commons (CRDC) Cloud Resources - Terra (FireCloud) and Seven Bridges-Cancer Genomics Cloud (SB-CGC) platforms - to perform automatic image segmentation with TotalSegmentator and pyradiomics feature extraction for a large cohort containing >126,000 CT volumes from >26,000 patients. Utilizing >21,000 Virtual Machines (VMs) over the course of the computation we completed analysis in under 9 hours, as compared to the estimated 522 days that would be needed on a single workstation. The total cost of utilizing the cloud for this analysis was $1,011.05. Our contributions include: 1) an evaluation of the numerous tradeoffs towards optimizing the use of cloud resources for large-scale image analysis; 2) CloudSegmentator, an open source reproducible implementation of the developed workflows, which can be reused and extended; 3) practical recommendations for utilizing the cloud for large-scale medical image computing tasks. We also share the results of the analysis: the total of 9,565,554 segmentations of the anatomic structures and the accompanying radiomics features in IDC as of release v18.

Investigating the structural network underlying brain-immune interactions using combined histopathology and neuroimaging: a critical review for its relevance in acute and long COVID-19.

Current views on immunity support the idea that immunity extends beyond defense functions and is tightly intertwined with several other fields of biology such as virology, microbiology, physiology and ecology. It is also critical for our understanding of autoimmunity and cancer, two topics of great biological relevance and for critical public health considerations such as disease prevention and treatment. Central to this review, the immune system is known to interact intimately with the nervous system and has been recently hypothesized to be involved not only in autonomic and limbic bio-behaviors but also in cognitive function. Herein we review the structural architecture of the brain network involved in immune response. Furthermore, we elaborate upon the implications of inflammatory processes affecting brain-immune interactions as reported recently in pathological conditions due to SARS-Cov-2 virus infection, namely in acute and post-acute COVID-19. Moreover, we discuss how current neuroimaging techniques combined with ad hoc clinical autopsies and histopathological analyses could critically affect the validity of clinical translation in studies of human brain-immune interactions using neuroimaging. Advances in our understanding of brain-immune interactions are expected to translate into novel therapeutic avenues in a vast array of domains including cancer, autoimmune diseases or viral infections such as in acute and post-acute or Long COVID-19.

TA2Viewer: A web-based browser for Terminologia Anatomica and online anatomical knowledge.

TA2Viewer is an open-access, web-based application and database for browsing anatomical terms and associated medical information on a computer or mobile device (https://ta2viewer.openanatomy.org/). It incorporates the official digital version of the second edition of Terminologia Anatomica (TA2) as published by the Federative International Programme for Anatomical Terminology (FIPAT), and adopted by the International Federation of Associations of Anatomists (IFAA) and other associations. It provides a dynamic and interactive view of the Latin and English nomenclatures. The organizational hierarchy of the terminology can be navigated by using a scrollable, expandable, and collapsible structured listing. Interactive search includes the official TA2 terms, synonyms, and related terms. TA2Viewer also uses TA2 term information to provide convenient access to other online resources, including Google web and image searches, PubMed, and Radiopaedia. Using cross-references from Wikidata, which were provided by the Wikipedia community, TA2Viewer offers links to Wikipedia, UBERON, UMLS, FMA, MeSH, NeuroNames, the public domain 20th edition of Gray's Anatomy, and other data sources. In addition, it can optionally use unofficial synonyms from Wikidata to provide multilingual term searches in hundreds of languages. By leveraging TA2, TA2Viewer provides free access to a curated anatomical nomenclature and serves as an index of online anatomical knowledge.

NCI Cancer Research Data Commons: Resources to Share Key Cancer Data.

Since 2014, the NCI has launched a series of data commons as part of the Cancer Research Data Commons (CRDC) ecosystem housing genomic, proteomic, imaging, and clinical data to support cancer research and promote data sharing of NCI-funded studies. This review describes each data commons (Genomic Data Commons, Proteomic Data Commons, Integrated Canine Data Commons, Cancer Data Service, Imaging Data Commons, and Clinical and Translational Data Commons), including their unique and shared features, accomplishments, and challenges. Also discussed is how the CRDC data commons implement Findable, Accessible, Interoperable, Reusable (FAIR) principles and promote data sharing in support of the new NIH Data Management and Sharing Policy. See related articles by Brady et al., p. 1384, Pot et al., p. 1396, and Kim et al., p. 1404.

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.

Enrichment of lung cancer computed tomography collections with AI-derived annotations.

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections containing computed tomography images of the chest, NSCLC-Radiomics, and a subset of the National Lung Screening Trial. Using publicly available AI algorithms, we derived volumetric annotations of thoracic organs-at-risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can aid in cancer imaging.

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Evaluation of mediastinal lymph node segmentation of heterogeneous CT data with full and weak supervision.

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use uni-directional or bi-directional measurements. Segmentation in 3D can provide more accurate evaluations of the lymph node size. Fully convolutional neural networks (FCNs) have achieved state-of-the-art results in segmentation for numerous medical imaging applications, including lymph node segmentation. Adoption of deep learning segmentation models in clinical trials often faces numerous challenges. These include lack of pixel-level ground truth annotations for training, generalizability of the models on unseen test domains due to the heterogeneity of test cases and variation of imaging parameters. In this paper, we studied and evaluated the performance of lymph node segmentation models on a dataset that was completely independent of the one used to create the models. We analyzed the generalizability of the models in the face of a heterogeneous dataset and assessed the potential effects of different disease conditions and imaging parameters. Furthermore, we systematically compared fully-supervised and weakly-supervised methods in this context. We evaluated the proposed methods using an independent dataset comprising 806 mediastinal lymph nodes from 540 unique patients. The results show that performance achieved on the independent test set is comparable to that on the training set. Furthermore, neither the underlying disease nor the heterogeneous imaging parameters impacted the performance of the models. Finally, the results indicate that our weakly-supervised method attains 90%- 91% of the performance achieved by the fully supervised training.

Comparison of physics-based deformable registration methods for image-guided neurosurgery.

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration.

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 'mental fog' to post-acute COVID-19 syndrome's executive function alteration: Implications for clinical approach.

A common symptom of the neuropsychiatric Post-Acute COVID-19 syndrome (neuro-PACS) is the so called 'brain fog'. Patients describe the brain fog as problems with attention, memory and mental fatigue. Brain fog is experienced by 9-55% of people for months after having contracted SARS-CoV-2 virus. Several theories have been proposed to explain PACS's brain fog, including a neuroinflammatory hypothesis, but the hypothesis remains to be proven. Here, we examined inflammatory and immunological blood profile in a cohort of patients with PACS to investigate the association between executive functions and blood inflammatory markers. Executive function was assessed by the Trail Making Test (TMT) Part A and Part B, as well as the Barkley Deficits in Executive Functioning Scale (BDEFS), in 71 patients (36 men), average age of 40 years (range: 15-82, SD: 15.7). Impairment in executive functioning (BDEFS scores and TMT B scores) correlated with increased levels of Interleukin-6 (IL-6), fibrinogen and ferritin. Moreover, elevated levels of Il-6, fibrinogen, ferritin, tumor necrosis factor-alpha and C-reactive protein have been observed in PACS. These findings demonstrate that PACS is characterized by the presence of an immuno-inflammatory process, which is associated with diminished executive functioning. Here, we argue in favour of a shift from the non-descriptive definition of 'mental fog' to a characterization of a subtype of PACS, associated with alteration in executive functioning. Implication for clinical settings and prevention are discussed.

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology.

BACKGROUND AND OBJECTIVES: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. METHODS: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. RESULTS: The results of different runs of the same experiment were reproducible to a large extent. However, we observed occasional, small variations in AUC values, indicating a practical limit to reproducibility. CONCLUSIONS: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects.

Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.

A Review of Low-Cost Ultrasound Compatible Phantoms.

Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost phantoms were provided. Overall, this article aims to summarize a breadth of ultrasound-compatible phantom research to enable informed phantom methods selection.

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.