Greater burden of white matter lesions and silent infarcts ipsilateral to carotid stenosis.

OBJECTIVES: Carotid stenosis may cause silent cerebrovascular disease (CVD) through atheroembolism and hypoperfusion. If so, revascularization may slow progression of silent CVD. We aimed to compare the presence and severity of silent CVD to the degree of carotid bifurcation stenosis by cerebral hemisphere. MATERIALS AND METHODS: Patients age ≥40 years with carotid stenosis >50% by carotid ultrasound who underwent MRI brain from 2011-2015 at Mayo Clinic were included. Severity of carotid stenosis was classified by carotid duplex ultrasound as 50-69% (moderate), 70-99% (severe), or occluded. White matter lesion (WML) volume was quantified using an automated deep-learning algorithm applied to axial T2 FLAIR images. Differences in WML volume and prevalent silent infarcts were compared across hemispheres and severity of carotid stenosis. RESULTS: Of the 183 patients, mean age was 71±10 years, and 39.3% were female. Moderate stenosis was present in 35.5%, severe stenosis in 46.5% and occlusion in 18.0%. Patients with carotid stenosis had greater WML volume ipsilateral to the side of carotid stenosis than the contralateral side (mean difference, 0.42±0.21cc, p=0.046). Higher degrees of stenosis were associated with greater hemispheric difference in WML volume (moderate vs. severe; 0.16±0.27cc vs 0.74±0.31cc, p=0.009). Prevalence of silent infarct was 23.5% and was greater on the side of carotid stenosis than the contralateral side (hemispheric difference 8.8%±3.2%, p=0.006). Higher degrees of stenosis were associated with higher burden of silent infarcts (moderate vs severe, 10.8% vs 31.8%; p=0.002). CONCLUSIONS: WML and silent infarcts were greater on the side of severe carotid stenosis.

Trends and Predictors of Imaging Utilization by Modality within an Academic Health System's Active Patient Population.

RATIONALE AND OBJECTIVES: Evaluate trends and demographic predictors of imaging utilization at a university-affiliated health system. MATERIALS AND METHODS: In this single-institution retrospective study, per capita estimates of imaging utilization among patients active in the health system were computed by cross-referencing all clinical encounters (2004-2016) for 1,628,980 unique patients with a listing of 6,157,303 diagnostic radiology encounters. Time trends in imaging utilization and effects of gender, race/ethnicity, and age were assessed, with subgroup analyses performed by imaging modality. Utilization was analyzed as both a continuous and binary outcome variable. RESULTS: Over 13 years, total diagnostic exams rose 6.8% a year (285,947-622,196 exams per annum), while the active population size grew 7.0% a year (244,238-543,290 active patients per annum). Per capita utilization peaked in 2007 at 1.33 studies/patient/year before dropping to 1.06 from 2011 to 2015. Latest per capita utilization was 0.22 for computed tomography, 0.10 for MR, 0.20 for US, 0.03 for NM, 0.51 for radiography, and 0.07 for mammography. Over the study period, ultrasound utilization doubled, whereas NM and radiography utilization decreased. computed tomography, MR, and mammography showed no significant net change. Univariate analysis of utilization as a continuous variable showed statistically significant effects of gender, race/ethnicity, and age (P < 0.0001), with utilization higher in males and Blacks and lower in Asian/Pacific Islanders and Hispanics. Utilization increased with age, except for a decline after age 75. Many of the effects of age, gender, and race/ethnicity were also found when analyzing the binarized utilization variable. CONCLUSIONS: Although absolute counts of imaging studies more than doubled, the net change in per capita utilization over the study period was minimal. Variations in utilization across age, gender, and race/ethnicity may reflect differential health needs and/or access disparities, warranting future studies.

Constrained generative adversarial network ensembles for sharable synthetic medical images.

Purpose: Sharing medical images between institutions, or even inside the same institution, is restricted by various laws and regulations; research projects requiring large datasets may suffer as a result. These limitations might be addressed by an abundant supply of synthetic data that (1) are representative (i.e., the synthetic data could produce comparable research results as the original data) and (2) do not closely resemble the original images (i.e., patient privacy is protected). We introduce a framework that generates data with these requirements leveraging generative adversarial network (GAN) ensembles in a controlled fashion. Approach: To this end, an adaptive ensemble scaling strategy with the objective of representativeness is defined. A sampled Fréchet distance-based constraint was then created to eliminate poorly converged candidates. Finally, a mutual information-based validation metric was embedded into the framework to confirm there are visual differences between the original and the generated synthetic images. Results: The applicability of the solution is demonstrated with a case study for generating three-dimensional brain metastasis (BM) from T1-weighted contrast-enhanced MRI studies. A previously published BM detection system was reported to produce 9.12 false-positives at 90% detection sensitivity based on the original data. By using the synthetic data generated with the proposed framework, the system produced 9.53 false-positives at the same sensitivity level. Conclusions: Achieving comparable algorithm performance relying solely on synthetic data unveils a significant potential to eliminate/reduce patient privacy concerns when sharing data in medical imaging.

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.

Bone grafting history affects soft tissue healing following implant placement.

BACKGROUND: This study aimed to determine and compare soft tissue healing outcomes following implant placement in grafted (GG) and non-grafted bone (NGG). METHODS: Patients receiving single implant in a tooth-bound maxillary non-molar site were recruited. Clinical healing was documented. Volume and content of wound fluid (WF; at 3, 6, and 9 days) were compared with adjacent gingival crevicular fluid (GCF; at baseline, 1, and 4 months). Buccal flap blood perfusion recovery and changes in bone thickness were recorded. Linear mixed model regression analysis and generalized estimating equations with Bonferroni adjustments were conducted for repeated measures. RESULTS: Twenty-five patients (49 ± 4 years; 13 males; nine NGG) completed the study. Soft tissue closure was slower in GG (P < 0.01). Differential response in WF/GCF protein concentrations was detected for ACTH (increased in GG only) and insulin, leptin, osteocalcin (decreased in NGG only) at day 6 (P ≤0.04), with no inter-group differences at any time(P > 0.05). Blood perfusion rate decreased immediately postoperatively (P < 0.01, GG) followed by 3-day hyperemia (P > 0.05 both groups). The recovery to baseline values was almost complete for NGG whereas GG stayed ischemic even at 4 months (P = 0.05). Buccal bone thickness changes were significant in GG sites (P ≤ 0.05). CONCLUSION: History of bone grafting alters the clinical, physiological, and molecular healing response of overlying soft tissues after implant placement surgery.

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as "compatible pairs" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.

Predicting rate of cognitive decline at baseline using a deep neural network with multidata analysis.

Purpose: Our study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit. Approach: We built a predictive model based on a supervised hybrid neural network utilizing a three-dimensional convolutional neural network to perform volume analysis of magnetic resonance imaging (MRI) and integration of nonimaging clinical data at the fully connected layer of the architecture. The experiments are conducted on the Alzheimer's Disease Neuroimaging Initiative dataset. Results: Experimental results confirm that there is a correlation between cognitive decline and the data obtained at the first visit. The system achieved an area under the receiver operator curve of 0.70 for cognitive decline class prediction. Conclusion: To our knowledge, this is the first study that predicts "slowly deteriorating/stable" or "rapidly deteriorating" classes by processing routinely collected baseline clinical and demographic data [baseline MRI, baseline mini-mental state examination (MMSE), scalar volumetric data, age, gender, education, ethnicity, and race]. The training data are built based on MMSE-rate values. Unlike the studies in the literature that focus on predicting mild cognitive impairment (MCI)-to-Alzheimer's disease conversion and disease classification, we approach the problem as an early prediction of cognitive decline rate in MCI patients.

Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network.

We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to model pathological changes (e.g., atherosclerotic plaques) in coronary vessels. The system learns the discriminatory features between vessels with and without atherosclerosis. The discriminative features at the final convolutional layer are visualized with a saliency map approach to provide visual clues related to atherosclerosis likelihood and location. We have evaluated the system on a reference dataset representing 247 patients with atherosclerosis and 246 patients free of atherosclerosis. With five fold cross-validation, an Accuracy = 90.9%, Positive Predictive Value = 58.8%, Sensitivity = 68.9%, Specificity of 93.6%, and Negative Predictive Value (NPV) = 96.1% are achieved at the artery/branch level with threshold 0.5. The average area under the receiver operating characteristic curve is 0.91. The system indicates a high NPV, which may be potentially useful for assisting interpreting physicians in excluding coronary atherosclerosis in patients with acute chest pain.

Integrating AI into radiology workflow: levels of research, production, and feedback maturity.

We present a roadmap for integrating artificial intelligence (AI)-based image analysis algorithms into existing radiology workflows such that (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI, and (2) radiologists' feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where (1) research enables the visualization of AI-based results/annotations by radiologists without generating new patient records; (2) production allows the AI-based system to generate results stored in an institution's picture-archiving and communication system; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.e., detection of brain metastases with T1-weighted contrast-enhanced three-dimensional MRI) illustrates the deployment details of a particular AI-based application according to the aforementioned maturity levels. It is shown that the given AI application significantly improves with feedback coming from radiologists; the number of incorrectly detected brain metastases (false positives) decreases from 14.2 to 9.12 per patient with the number of subsequently annotated datasets increasing from 93 to 217 as a result of radiologist adjudication.

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.

A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence.

PURPOSE: To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging. MATERIALS AND METHODS: GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and three-dimensional segmentation, quantification, and classification. To assess radiologist engagement and performance efficiency associated with GUI-related capabilities, image annotation rate (studies per day) and speed (minutes per case) were evaluated in two clinical scenarios of varying complexity: hip fracture detection and coronary atherosclerotic plaque demarcation and stenosis grading. RESULTS: For hip fracture, 1050 radiographs were annotated over 7 days (150 studies per day; median speed: 10 seconds per study [interquartile range, 3-21 seconds per study]). A total of 294 coronary CT angiographic studies with 1843 arteries and branches were annotated for atherosclerotic plaque over 23 days (15.2 studies [80.1 vessels] per day; median speed: 6.08 minutes per study [interquartile range, 2.8-10.6 minutes per study] and 73 seconds per vessel [interquartile range, 20.9-155 seconds per vessel]). CONCLUSION: GUI-component compatibility with common image analysis tools facilitates radiologist engagement in image data curation, including image annotation, supporting AI application development and evolution for medical imaging. When complemented by other GUI elements, a continuous integrated workflow supporting formation of an agile deep neural network life cycle results.Supplemental material is available for this article.© RSNA, 2019.

Diagnostic Accuracy of Digital Breast Tomosynthesis in the Evaluation of Palpable Breast Abnormalities.

RATIONALE AND OBJECTIVES: The role of digital breast tomosynthesis (DBT) in evaluating palpable abnormalities has not been evaluated and its accuracy compared to 2D mammography is unknown. The purpose of this study was to evaluate combined 2D mammography, DBT, and ultrasound (US) at palpable sites. MATERIALS AND METHODS: Two breast imagers reviewed blinded consecutive cases with combined 2D mammograms and DBT examinations performed for palpable complaints. By consensus, 2D and DBT findings were recorded and compared to US. Patient characteristics, demographics, subsequent workup, and outcome were recorded. RESULTS: A total of 229 sites in 188 patients were included, with 50 biopsies performed identifying 18 cancers. All 18 cancers were identified on 2D and US, whereas 17 cancers were identified on DBT. Cancer detection sensitivities for 2D, DBT, and US were 100.0%, 94.4%, and 100.0%. The negative predictive value, when combined with US, was 100% for both. The sensitivity and the specificity for both benign and malignant findings with 2D and DBT were 70.5% versus 75.4% (P = 0.07) and 95.3% versus 99.1% (P = 0.125). Palpable findings not identified by 2D and DBT were smaller than those identified (11.5 ± 8.3 mm vs 23.9 ± 12.8 mm, P < 0.001). Patients with dense breasts were more likely to have mammographically occult findings than patients with nondense breasts (27.4% vs 8.3%). CONCLUSIONS: DBT did not improve cancer detection over 2D or US. Both mammographic modalities failed to identify sonographically confirmed findings primarily in dense breasts. The diagnostic use of DBT at palpable sites provided limited benefit over combined 2D and US. When utilizing DBT, US should be performed to adequately characterize palpable sites.

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.

Gingival Perfusion and Tissue Biomarkers During Early Healing of Postextraction Regenerative Procedures: A Prospective Case Series.

BACKGROUND: Postextraction alveolar bone loss, mostly affecting the buccal plate, occurs despite regenerative procedures. To better understand possible determinants, this prospective case series assesses gingival blood perfusion and tissue molecular responses in relation to postextraction regenerative outcomes. METHODS: Adults scheduled to receive bone grafting in maxillary, non-molar, single-tooth extraction sites were recruited. Clinical documentation included the following: 1) probing depth (PD); 2) keratinized tissue width (KT); 3) tissue biotype (TB); and 4) plaque level. Wound closure was clinically evaluated. Gingival blood perfusion was measured by laser Doppler flowmetry (LDF). Wound fluid (WF) and gingival biopsies were analyzed for protein levels and gene expression, respectively, of relevant molecular markers. Bone healing outcomes were determined radiographically (cone-beam computed tomography). Healing was followed for 4 months. RESULTS: Data from 15 patients are reported. Postoperatively, neither complications nor changes in PD, KT, or TB were observed. LDF revealed decreased perfusion followed by hyperemia that persisted for 1 month (P ≤0.05). WF levels of angiopoietin-2, interleukin-8 (IL-8), tumor necrosis factor-alpha (TNF-α), and vascular endothelial growth factor peaked on day 6 (P ≤0.05) and decreased thereafter. Only IL-8 and TNF-α exhibited increased gene expression. Linear bone changes were negligible. Volumetric bone changes were minimal but statistically significant, with more bone loss when membrane was used (P = 0.05). CONCLUSIONS: Gingival blood perfusion after postextraction bone regenerative procedures follows an ischemia-reperfusion model. Transient increases in angiogenic factor levels and prolonged hyperemia characterize the soft tissue response. These soft tissue responses do not determine radiographic bone changes.

CT Gray-Level Texture Analysis as a Quantitative Imaging Biomarker of Epidermal Growth Factor Receptor Mutation Status in Adenocarcinoma of the Lung.

OBJECTIVE: The purpose of this study was to investigate the radiogenomic correlation between CT gray-level texture features and epidermal growth factor receptor (EGFR) mutation status in adenocarcinoma of the lung. MATERIALS AND METHODS: This retrospective study included 25 patients with exon 19 short inframe deletion (exon 19) and 21 patients with exon 21 L858R point (exon 21) EGFR mutations among 125 patients with EGFR mutant adenocarcinoma of the lung. The randomly formed control group consisted of 20 patients selected from 126 patients with EGFR mutation-negative (wild-type) adenocarcinomas. Five gray-level texture features (contrast, correlation, inverse difference moment, angular second moment, and entropy) were analyzed. RESULTS: Contrast differentiated both exon 19 (p = 0.00027) and exon 21 (p = 0.00001) mutants from the wild type. Wild-type adenocarcinomas had high scores for contrast (mean, 1598.547) compared with EGFR mutants (mean, 679.463). Correlation differentiated both exon 19 (p = 0.017) and exon 21 (p = 0.0015) mutants from wild-type adenocarcinomas. Inverse difference moment differentiated exon 19 mutants from exon 21 mutants (p = 0.019) and both exon 19 (p = 0.044) and exon 21 (p = 0.00001) mutants from wild-type adenocarcinomas. Angular second moment and entropy were not associated with statistically significant differences between mutation statuses. CONCLUSION: Contrast, correlation, and inverse difference moment texture features correlate with EGFR mutation status in adenocarcinoma of the lung. Further investigation with larger prospective studies is needed to validate the role of CT gray-level texture analysis as a quantitative imaging biomarker.

Unexpectedly high prevalence of sarcoidosis in a representative U.S. Metropolitan population.

The prevalence of sarcoidosis in the United States is unknown, with estimates ranging widely from 1 to 40 per 100,000. We sought to determine the prevalence of sarcoidosis in our health system compared to other rare lung diseases and to further establish if the prevalence was changing over time. We interrogated the electronic medical records of all patients treated in our health system from 1995 to 2010 (1.48 million patients) using the common ICD9 codes for sarcoidosis (135), lung cancer (162), and several other lung diseases characterized, like sarcoidosis, as "rare lung diseases". The patient demographic information (race, gender, age) was further analyzed to identify signature data patterns. The prevalence of sarcoidosis in our health system increased steadily from 164/100,000 in 1995 to 330/100,000 in 2010, and this trend could not be ascribed simply to changes in patient demographics or patient referral patterns. We further estimate that the prevalence of sarcoidosis exceeds 48 per 100,000 in Franklin County, Ohio, the demographic profile of which is nearly identical to that of the U.S. Sarcoidosis prevalence increased over time relative to lung cancer, a benchmark disease with stable disease prevalence, and exceeded that of other rare lung diseases. We postulate that the observed 2-fold increase in sarcoidosis disease prevalence in our health system is primarily related to improved detection and diagnostic approaches, and we conclude that the actual prevalence of sarcoidosis in central Ohio greatly exceeds current U.S. estimates.