Endoscopic endonasal marsupialization of rathke cleft cysts: clinical outcomes and risk factors analysis of visual impairment, pituitary dysfunction, and CSF leak.

OBJECTIVE: This paper assesses the clinical and imaging characteristics, histopathological findings, and treatment outcomes of patients with Rathke's cleft cyst (RCC), as well as identifies potential risk factors for preoperative visual and pituitary dysfunction, intraoperative cerebrospinal fluid (CSF) leak, and recurrence. Through analyzing these factors, the study aims to contribute to the current understanding of the management of RCCs and identify opportunities for improving patient outcomes. METHODS: We performed a retrospective analysis of 45 RCC patients between ages 18-80 treated by Endoscopic Endonasal Approach (EEA) and cyst marsupialization between 2010 and 2022 at a single institution. RESULTS: The median patient age was 34, and 73% were female. The mean follow-up was 70 ± 43 months. Preoperative visual impairment correlated with cyst diameter (OR = 1.41, 95% CI = 1.07 to 1.85, p-value = 0.01) and older age (OR = 1.06, 95% CI = 1.01 to 1.11, p-value = 0.02). Intraoperative CSF leaks were 11 times more likely for cysts ≥ 2 cm (OR = 11.3, 95% CI = 1.25 to 97.37, p-value = 0.03), with the odds of leakage doubling for every 0.1 cm increase in cyst size (OR = 1.41, 95% CI = 1.08 to 1.84, p-value = 0.01). Preoperative RCC appearing hypointense on T1 images demonstrated significantly higher CSF leak rates than hyperintense lesions (OR = 122.88, 95% CI = 1.5 to 10077.54, p-value = 0.03). Preoperative pituitary hypofunction was significantly more likely in patients with the presence of inflammation on histopathology (OR = 20.53, 95% CI = 2.20 to 191.45, p-value = 0.008 ) and T2 hyperintensity on magnetic resonance imaging (MRI) sequences (OR = 23.2, 95% CI = 2.56 to 211.02, p-value = 0.005). Notably, except for the hyperprolactinemia, no postoperative improvement was observed in pituitary function. CONCLUSION: Carefully considering risk factors, surgeons can appropriately counsel patients and deliver expectations for complications and long-term results. In contrast to preoperative visual impairment, preoperative pituitary dysfunction was found to have the least improvement post-surgery. It was the most significant permanent complication, with our data indicating the link to the cyst signal intensity on T2 MR and inflammation on histopathology. Earlier surgical intervention might improve the preservation of pituitary function.

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Two data-driven algorithms were developed for detecting and characterizing Inferior Vena Cava (IVC) filters on abdominal computed tomography to assist healthcare providers with the appropriate management of these devices to decrease complications: one based on 2-dimensional data and transfer learning (2D + TL) and an augmented version of the same algorithm which accounts for the 3-dimensional information leveraging recurrent convolutional neural networks (3D + RCNN). The study contains 2048 abdominal computed tomography studies obtained from 439 patients who underwent IVC filter placement during the 10-year period from January 1st, 2009, to January 1st, 2019. Among these, 399 patients had retrievable filters, and 40 had non-retrievable filter types. The reference annotations for the filter location were obtained through a custom-developed interface. The ground truth annotations for the filter types were determined based on the electronic medical record and physician review of imaging. The initial stage of the framework returns a list of locations containing metallic objects based on the density of the structure. The second stage processes the candidate locations and determines which one contains an IVC filter. The final stage of the pipeline classifies the filter types as retrievable vs. non-retrievable. The computational models are trained using Tensorflow Keras API on an Nvidia Quadro GV100 system. We utilized a fine-tuning supervised training strategy to conduct our experiments. We find that the system achieves high sensitivity on detecting the filter locations with a high confidence value. The 2D + TL model achieved a sensitivity of 0.911 and a precision of 0.804, and the 3D + RCNN model achieved a sensitivity of 0.923 and a precision of 0.853 for filter detection. The system confidence for the IVC location predictions is high: 0.993 for 2D + TL and 0.996 for 3D + RCNN. The filter type prediction component of the system achieved 0.945 sensitivity, 0.882 specificity, and 0.97 AUC score with 2D + TL and 0. 940 sensitivity, 0.927 specificity, and 0.975 AUC score with 3D + RCNN. With the intent to create tools to improve patient outcomes, this study describes the initial phase of a computational framework to support healthcare providers in detecting patients with retained IVC filters, so an individualized decision can be made to remove these devices when appropriate, to decrease complications. To our knowledge, this is the first study that curates abdominal computed tomography (CT) scans and presents an algorithm for automated detection and characterization of IVC filters.

Neck CT imaging and correlation with thyroid cancer incidence across age, gender and race.

OBJECTIVE: Incidental detection of thyroid cancers has been proposed as a cause of thyroid cancer increases over past decades, but few studies assess the impact of imaging utilization on thyroid cancer incidence. This study quantifies neck CT prevalence and its relationship with thyroid cancer incidence as a function of age, sex and race. DESIGN AND PATIENTS: Medical records of over 1 million patients at our institution were retrospectively analysed to quantify neck CT prevalence from 2004 to 2011 (study period). A national cancer database was used to compute thyroid cancer incidences over the study period and a reference period (1974-81) and to calculate change in thyroid incidence between the two periods. Both populations were partitioned into demographic subgroups of varying age, sex and race. Linear correlation between neck imaging and thyroid cancer incidence changes among subgroups was assessed using Pearson's correlation. RESULTS: Neck CT imaging and change in thyroid cancer incidence varied across all examined demographic variables, particularly age. When stratifying by age, CT use correlated strongly with recent national thyroid cancer incidence (R = .97) and with 30-year change in thyroid cancer incidence (R = .87). Across all demographic subgroups, CT prevalence correlated strongly and positively with change in thyroid cancer incidence (R = .60), greater for whites (R = .60) and blacks (R = .70) than other races (R = .28). CONCLUSION: Differences in neck CT usage strongly and positively correlates with the variation in thyroid cancer trends based on age, gender and race.

Predicting Mental Decline Rates in Mild Cognitive Impairment From Baseline MRI Volumetric Data.

PURPOSE: In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline. METHODS: The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe. Subjects with <24 months or <4 measurements of MMSE data were excluded. Predictive modeling of fast cognitive decline (defined as >0.6/year) from baseline volumetric data was performed on subjects with MCI using a single hidden layer neural network. RESULTS: Among 698 baseline MCI subjects, the median annual decline in the MMSE score was 1.3 for converters to dementia versus 0.11 for stable MCI (P<0.001). A 0.6/year threshold captured dementia conversion with 82% accuracy (sensitivity 79%, specificity 85%, area under the receiver operating characteristic curve 0.88). Regional volumes on baseline MRI predicted fast cognitive decline with a test accuracy of 71%. DISCUSSION: An MMSE score decrease of >0.6/year is associated with MCI-to-dementia conversion and can be predicted from baseline MRI.

Management of large intraventricular meningiomas with minimally invasive port technique: a three-case series.

The use of minimally invasive transcranial ports for the resection of deep-seated lesions has been shown to be safe and effective. To date, most of the literature regarding the tubular retractors used in brain surgery is comprised of individual case reports that describe the successful resection of deep-seated lesions such as thalamic pilocytic astrocytomas, colloid cysts in the third ventricle, hematomas, and cavernous angiomas. The authors describe their experience using a tubular retractor system with three different cases involving large intraventricular meningiomas and examine radiographic and patient outcomes. A single-institution, retrospective case series was performed from a skull base database. Patients who underwent resection of intraventricular > 4-cm meningiomas with port technology were identified. The authors reviewed three cases to illustrate the feasibility of minimal access port surgery for the resection of these lesions. Complete resection was achieved in all cases. None of the patients developed permanent neurological deficits. There were no major complications related to surgery and no mortalities. Good clinical and surgical outcomes for atrium meningiomas can be achieved through the minimally invasive port technique and tumor size does not appear to be a limitation.

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use.

Coronary computed tomography angiography (CCTA) evaluation of chest pain patients in an emergency department (ED) is considered appropriate. While a "negative" CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an artificial intelligence (AI) algorithm and workflow for assisting qualified interpreting physicians in CCTA screening for total absence of coronary atherosclerosis. The two-phase approach consisted of (1) phase 1-development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection, and (2) phase 2-simulated clinical Trialing of developed algorithm on a per-case (entire coronary artery tree) basis in a more "real-world" study population (n = 100 with 28% disease prevalence) from an ED chest pain series. This allowed pre-deployment evaluation of the AI-based CCTA screening application which provides vessel-by-vessel graphic display of algorithm inference results integrated into a clinically capable viewer. Algorithm performance evaluation used area under the receiver operating characteristic curve (AUC-ROC); confusion matrices reflected ground truth vs AI determinations. The vessel-based algorithm demonstrated strong performance with AUC-ROC = 0.96. In both phase 1 and phase 2, independent of disease prevalence differences, negative predictive values at the case level were very high at 95%. The rate of completion of the algorithm workflow process (96% with inference results in 55-80 s) in phase 2 depended on adequate image quality. There is potential for this AI application to assist in CCTA interpretation to help extricate atherosclerosis from chest pain presentations.

Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning.

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g., computed tomography). Targeting performance enhancement of a DNN algorithm based on a small image dataset, we assessed incremental impact of 3D-to-2D projection methods, one supporting novel data augmentation (DA); photometric grayscale-to-color conversion (GCC); and/or TL on training of an algorithm from a small coronary computed tomography angiography (CCTA) dataset (200 examinations, 50% with atherosclerosis and 50% atherosclerosis-free) producing 245 diseased and 1127 normal coronary arteries/branches. Volumetric CCTA data was converted to a 2D format creating both an Aggregate Projection View (APV) and a Mosaic Projection View (MPV), supporting DA per vessel; both grayscale and color-mapped versions of each view were also obtained. Training was performed both without and with TL, and algorithm performance of all permutations was compared using area under the receiver operating characteristics curve. Without TL, APV performance was 0.74 and 0.87 on grayscale and color images, respectively, compared to 0.90 and 0.87 for MPV. With TL, APV performance was 0.78 and 0.88 on grayscale and color images, respectively, compared with 0.93 and 0.91 for MPV. In conclusion, TL enhances performance of a DNN algorithm from a small volumetric dataset after proposed 3D-to-2D reformatting, but additive gain is achieved with application of either GCC to APV or the proposed novel MPV technique for DA.

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue.

Nationwide Trends in Use of Catheter-Directed Therapy for Treatment of Pulmonary Embolism in Medicare Beneficiaries from 2004 to 2016.

PURPOSE: To evaluate changes in the use of catheter-directed therapy (CDT) for pulmonary embolism (PE) treatment with attention to primary operator specialty in the Medicare population. METHODS: Using a 5% national sample of Medicare claims data from 2004 to 2016, all claims associated with PE were identified. The annual volume of 2 billable CDT services-arterial mechanical thrombectomy and transcatheter arterial infusion for thrombolysis-were determined to evaluate changes in CDT use and primary CDT operator specialty over time. RESULTS: The total number of CDT procedures increased over the course of the study period, representing 0.457 and 5.057 service counts per 100,000 Medicare beneficiaries in 2004 and 2016, respectively. The proportion of PEs treated with CDT increased 10-fold from 2004 to 2016, increasing from 0.1% to 1.0%. Interventional radiologists performed most CDT therapies each year, with the exception of 2010 when vascular surgeons performed more. In 2016, interventional radiologists performed 3.54 CDT services for PE per 100,000 Medicare beneficiaries, which was 70% of total CDT for PE procedures, followed by interventional cardiologists and vascular surgeons performing 0.92 services (18%) and 0.60 services (12%), respectively. CONCLUSIONS: CDT is an increasingly used treatment for PE, with a 10-fold increase from 2004 to 2016. Interventional radiologists are the dominant providers of these services, followed by interventional cardiologists and vascular surgeons.

CT for thoracic and lumbar spine fractures: Can CT findings accurately predict posterior ligament complex injury?

PURPOSE: This study aims to determine whether secondary CT findings can predict posterior ligament complex (PLC) injury in patients with acute thoracic (T) or lumbar (L) spine fractures. METHODS: This is a retrospective study of 105 patients with acute thoracic and lumbar spine fractures on CT, with MRI as the reference standard for PLC injury. Three readers graded CT for facet joint alignment (FJA), widening (FJW), pedicle or lamina fracture (PLF), spinous fracture (SPF), interspinous widening (ISW), vertebral translation (VBT), and posterior endplate fracture (PEF). Univariate and multivariate logistic regression analyses were performed separately for each reader to test for associations between CT and PLC injury, and diagnostic performance of CT was calculated. RESULTS: Fifty-three of 105 patients had PLC injury by MRI. Statistically significant predictors of PLC injury were VBT, PLF, ISW, and SPF. Using these four CT findings, odds of PLC injury ranged from 3.8 to 5.6 for one positive finding, but increased to 13.6-25.1 for two or more. At least one positive CT finding was found to yield average sensitivity of 82% and specificity 59%, while two or more yielded sensitivity 46% and specificity 88%. CONCLUSION: While no individual CT finding is sufficiently accurate to diagnose or exclude PLC injury, greater the number of positive CT findings (VBT, PLF, ISW, and SPF), the higher the odds of PLC injury. The presence of a single abnormal CT finding may warrant confirmatory MRI for PLC injury, while two or more CT findings may have adequate specificity to avoid need for MRI prior to surgical intervention. These slides can be retrieved under Electronic Supplementary Material.

Radiology and Enterprise Medical Imaging Extensions (REMIX).

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of "big imaging data," as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available.

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images. Examinations were processed by using a convolutional neural network (deep learning) using two different window and level configurations (brain window and stroke window). AI algorithm performance was tested on a separate dataset containing 50 examinations with HMH findings, 15 with SAI findings, and 35 with noncritical findings. Results Final algorithm performance for HMH showed 90% (45 of 50) sensitivity (95% confidence interval [CI]: 78%, 97%) and 85% (68 of 80) specificity (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 with the brain window. For SAI, the best performance was achieved with the stroke window showing 62% (13 of 21) sensitivity (95% CI: 38%, 82%) and 96% (27 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81. Conclusion AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings. © RSNA, 2017 Online supplemental material is available for this article.

MR Elastography-derived Stiffness: A Biomarker for Intervertebral Disc Degeneration.

Purpose To determine the repeatability of magnetic resonance (MR) elastography-derived shear stiffness measurements of the intervertebral disc (IVD) taken throughout the day and their relationship with IVD degeneration and subject age. Materials and Methods In a cross-sectional study, in vivo lumbar MR elastography was performed once in the morning and once in the afternoon in 47 subjects without current low back pain (IVDs = 230; age range, 20-71 years) after obtaining written consent under approval of the institutional review board. The Pfirrmann degeneration grade and MR elastography-derived shear stiffness of the nucleus pulposus and annulus fibrosus regions of all lumbar IVDs were assessed by means of principal frequency analysis. One-way analysis of variance, paired t tests, concordance and Bland-Altman tests, and Pearson correlations were used to evaluate degeneration, diurnal changes, repeatability, and age effects, respectively. Results There were no significant differences between morning and afternoon shear stiffness across all levels and there was very good technical repeatability between the morning and afternoon imaging results for both nucleus pulposus (R = 0.92) and annulus fibrosus (R = 0.83) regions. There was a significant increase in both nucleus pulposus and annulus fibrosus MR elastography-derived shear stiffness with increasing Pfirrmann degeneration grade (nucleus pulposus grade 1, 12.5 kPa ± 1.3; grade 5, 16.5 kPa ± 2.1; annulus fibrosus grade 1, 90.4 kPa ± 9.3; grade 5, 120.1 kPa ± 15.4), and there were weak correlations between shear stiffness and age across all levels (R ≤ 0.32). Conclusion Our results demonstrate that MR elastography-derived shear stiffness measurements are highly repeatable, weakly correlate with age, and increase with advancing IVD degeneration. These results suggest that MR elastography-derived shear stiffness may provide an objective biomarker of the IVD degeneration process. © RSNA, 2017 Online supplemental material is available for this article.

Implementing Machine Learning in Radiology Practice and Research.

OBJECTIVE: The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. CONCLUSION: Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

Automated critical test result notification system: architecture, design, and assessment of provider satisfaction.

OBJECTIVE: Communicating critical results of diagnostic imaging procedures is a national patient safety goal. The purposes of this study were to describe the system architecture and design of Alert Notification of Critical Results (ANCR), an automated system designed to facilitate communication of critical imaging results between care providers; to report providers' satisfaction with ANCR; and to compare radiologists' and ordering providers' attitudes toward ANCR. MATERIALS AND METHODS: The design decisions made for each step in the alert communication process, which includes user authentication, alert creation, alert communication, alert acknowledgment and management, alert reminder and escalation, and alert documentation, are described. To assess attitudes toward ANCR, internally developed and validated surveys were administered to all radiologists (n = 320) and ordering providers (n = 4323) who sent or received alerts 3 years after ANCR implementation. RESULTS: The survey response rates were 50.4% for radiologists and 36.1% for ordering providers. Ordering providers were generally dissatisfied with the training received for use of ANCR and with access to technical support. Radiologists were more satisfied with documenting critical result communication (61.1% vs 43.2%; p = 0.0001) and tracking critical results (51.6% vs 35.1%; p = 0.0003) than were ordering providers. Both groups agreed use of ANCR reduces medical errors and improves the quality of patient care. CONCLUSION: Use of ANCR enables automated communication of critical test results. The survey results confirm overall provider satisfaction with ANCR but highlight the need for improved training strategies for large numbers of geographically dispersed ordering providers. Future enhancements beyond acknowledging receipt of critical results are needed to help ensure timely and appropriate follow-up of critical results to improve quality and patient safety.

Four-year impact of an alert notification system on closed-loop communication of critical test results.

OBJECTIVE: One of the patient safety goals proposed by the Joint Commission urges hospitals to develop a policy for communicating critical test results and to measure adherence to that policy. We evaluated the impact of an alert notification system on policy adherence for communicating critical imaging test results to referring providers and assessed system adoption over the first 4 years after implementation. MATERIALS AND METHODS: This study was performed in a 753-bed academic medical center. The intervention, an automated alert notification system for critical results, was implemented in January 2010. The primary outcome was adherence to institutional policy for timely closed-loop communication of critical imaging results, and the secondary outcome was system adoption. Policy adherence was determined through manual review of a random sample of radiology reports from the first 4 years after the intervention (n = 37,604) compared with baseline outcomes 1 year before the intervention (n = 9430). Adoption was evaluated by quantifying the use of the system overall and the proportion of alerts that used noninterruptive communication as a percentage of all reports generated by 320 radiologists (n = 1,538,059). A statistical analysis of the trend at 6-month intervals over 4 years was performed using a chi-square trend test. RESULTS: Adherence to the policy increased from 91.3% before the intervention to 95.0% after the intervention (p < 0.0001). There was a ninefold increase in the critical results communicated via the system (chi-square trend test, p < 0.0001). During the first 4 years after the intervention, 41,445 alerts (41% of the total number of alerts) used the system's noninterruptive process for communicating less urgent critical results, which was substantially unchanged over the 4 years postintervention, thus reducing unnecessary paging interruptions. CONCLUSION: An automated alert notification system for communicating critical imaging results was successfully adopted and was associated with increased adherence to institutional policy for communicating critical test results and with reduced workflow interruptions.

Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence.

In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.

CT and MR utilization and morbidity metrics across Body Mass Index.

OBJECTIVE: Obesity is a high-morbidity chronic condition and risk factor for multiple diseases that necessitate imaging. This study assesses the relationship between BMI and same-year utilization of CT and MR imaging in a large healthcare population. METHODS: In this retrospective population-based study, all patients aged ≥18 years with a documented BMI in the multi-institutional Cosmos database were included. Cohorts were identified based on ≥1 documented BMI in 2021 within pre-defined ranges. For each cohort, we assessed the percentage of patients undergoing head, neck, chest, spine, or abdomen/pelvis CT and MR during the same year. Disease severity was quantified based on emergency department (ED) visits and mortality. RESULTS: In our population of 49.6 million patients, same-year CT and MR utilization was 14.5 ±0.01% and 6.0±0.01%, respectively. The underweight cohort had the highest CT (25.8±0.1%) and MR (8.01 ± 0.05) imaging utilization. At high extremes of BMI (>50 kg/m2), CT utilization mildly increased (18.4±0.1%), but MR utilization decreased (5.3±0.04%). While morbidity differences may explain some BMI-utilization relationships, lower MR utilization in the BMI>50 cohort contrasts with higher age-adjusted mortality (1.8±0.03%) and ED utilization (32.4±0.1%) in this cohort relative to normal weight (1.5±0.01% and 25.7±0.02%, respectively). CONCLUSION: Underweight patients had disproportionately high CT/MR utilization, and high extremes of BMI are associated with mildly higher CT and lower MR utilization than the normal weight cohort. The elevated mortality and ED utilization in severely obese patients contrasts with their lower MR imaging utilization. Our findings may assist public health efforts to accommodate obesity trends.

Quantifying effects of blood pressure control on neuroimaging utilization in a large multi-institutional healthcare population.

OBJECTIVES: Essential hypertension is a common chronic condition that can exacerbate or complicate various neurological diseases that may necessitate neuroimaging. Given growing medical imaging costs and the need to understand relationships between population blood pressure control and neuroimaging utilization, we seek to quantify the relationship between maximum blood pressure recorded in a given year and same-year utilization of neuroimaging CT or MR in a large healthcare population. METHODS: A retrospective population-based cohort study was performed by extracting aggregate data from a multi-institutional dataset of patient encounters from 2016, 2018, and 2020 using an informatics platform (Cosmos) consisting of de-duplicated data from over 140 academic and non-academic health systems, comprising over 137 million unique patients. A population-based sample of all patients with recorded blood pressures of at least 50 mmHg DBP or 90 mmHg SBP were included. Cohorts were identified based on maximum annual SBP and DBP meeting or exceeding pre-defined thresholds. For each cohort, we assessed neuroimaging CT and MR utilization, defined as the percentage of patients undergoing ≥1 neuroimaging exam of interest in the same calendar year. RESULTS: The multi-institutional population consisted of >38 million patients for the most recent calendar year analyzed, with overall utilization of 3.8-5.1% for CT and 1.5-2.0% for MR across the study period. Neuroimaging utilization increased substantially with increasing annual maximum BP. Even a modest BP increase to 140 mmHg systolic or 90 mmHg diastolic is associated with 3-4-fold increases in MR and 5-7-fold increases in CT same-year imaging compared to BP values below 120 mmHg / 80 mmHg. CONCLUSION: Higher annual maximum recorded blood pressure is associated with higher same-year neuroimaging CT and MR utilization rates. These observations are relevant to public health efforts on hypertension management to mitigate costs associated with growing imaging utilization.