scholarly journals Artificial Intelligence Algorithm-Based CTA Imaging for Diagnosing Ischemic Type Biliary Lesions after Orthotopic Liver Transplantation

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Zhenxing Yu ◽  
Guixue Ou ◽  
Ruihua Wang ◽  
Qinghua Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Liver Transplantation ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Orthotopic Liver Transplantation ◽  
Clinical Application ◽  
Predictive Value ◽  
Early Stage ◽  
Ischemic Type Biliary Lesions

The study focused on the clinical application value of artificial intelligence-based computed tomography angiography (CTA) in the diagnosis of orthotopic liver transplantation (OLT) after ischemic type biliary lesions (ITBL). A total of 66 patients receiving OLT in hospital were selected. Convolutional neural network (CNN) algorithm was used to denoise and detect the edges of CTA images of patients. At the same time, the quality of the processed image was subjectively evaluated and quantified by Hmax, Ur, Cr, and other indicators. Then, the digital subtraction angiography (DSA) diagnosis and CTA diagnosis based on CNN were compared for the sensitivity, specificity, positive predictive value, negative predictive value, and patient classification results. It was found that CTA can clearly reflect the information of hepatic aorta lesions and thrombosis in patients with ischemic single-duct injury after liver transplantation. After neural network algorithm processing, the image quality is obviously improved, the lesions are more prominent, and the details of lesion parts are also well displayed. ITBL occurred in 40 (71%) of 56 patients with abnormal CTA at early stage. ITBL occurred in only 8 (12.3%) of 65 patients with normal CTA at early stage. Early CTA manifestations had high sensitivity (72.22%), specificity (87.44%), positive predictive value (60.94%), and negative predictive value (92.06%) for the diagnosis of ITBL. It was concluded that artificial intelligence-based CTA had high clinical application value in the diagnosis of ITBL after OLT.

scholarly journals AIBx, artificial intelligence model to risk stratify thyroid nodules

2021 ◽  
Author(s):  
Johnson Thomas ◽  
Tracy Haertling
Keyword(s):  
Artificial Intelligence ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Classification Systems ◽  
Image Similarity ◽  
Ultrasound Images ◽  
Similarity Model ◽  
Sensitivity Specificity

AbstractBackgroundCurrent classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. 1 out of 2 women over the age of 50 may have a thyroid nodule and at present the only way to exclude malignancy is through invasive procedures. Hence, there exists a need for noninvasive objective classification of thyroid nodules. Some cancers have benign appearance on ultrasonogram. Hence, we decided to create an image similarity algorithm rather than image classification algorithm.MethodsUltrasound images of thyroid nodules from patients who underwent either biopsy or thyroid surgery from February of 2012 through February of 2017 in our institution were used to create AI models. Nodules were excluded if there was no definitive diagnosis of benignity or malignancy. 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models. Later, these AI models were used to test 103 thyroid nodules which underwent biopsy or surgery from March of 2017 through July of 2018.ResultsNegative predictive value of the image similarity model was 93.2%. Sensitivity, specificity, positive predictive value and accuracy of the model was 87.8%, 78.5%, 65.9% and 81.5% respectively.ConclusionWhen compared to published results of ACR TIRADS and ATA classification system, our image similarity model had comparable negative predictive value with better sensitivity specificity and positive predictive value. By using image similarity AI models, we can eliminate subjectivity and decrease the number of unnecessary biopsies. Using image similarity AI model, we were able to create an explainable AI model which increases physician’s confidence in the predictions.

scholarly journals Detection of Lung Cancer using Multilayer Perceptron Neural Network

2017 ◽  
Vol 1 (4) ◽  
pp. 109
Author(s):  
Farzad Mirzakhani
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Predictive Value ◽  
Artificial Neural Network Model

Introduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary. Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1. Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software. Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%. Conclusion: In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer.

The  novel biomarker HE4 versus CA125 in detecting endometrial cancer: A case control prospective study.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 5094-5094
Author(s):  
Francesco Plotti ◽  
Marzio Angelo Zullo ◽  
Michela Angelucci ◽  
Irma Oronzi ◽  
Patrizio Damiani ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Early Stage ◽  
Statistical Significance ◽  
University Campus ◽  
Serum Samples ◽  
Benign Pathology

5094 Background: In endometrial cancer, there are no markers routinely used in clinical practice. This study prospectively investigates the sensitivity and specificity of new marker HE4 in detection of endometrial cancer. Methods: Serum samples were prospectively obtained 24 hours before surgery from 25 patients with endometrial cancer and from 25 patients with uterine benign pathology, operated from January 2011 to October 2011 at University Campus Bio-Medico of Rome. Preoperative CA125 levels were evaluated by a one-step “sandwich” radioimmunoassay. HE4 levels were determined using the HE4 enzymatic immune assay. The CA125 normal value is considered less than 35 U/mL. Two HE4 cut-off are considered: less than 70 pmol/L and less than 150 pmol/L. The specificity analysis was performed using the parametric T-Test for comparing the HE4 series and the Mann-Whitney test for the CA125 series. The level of statistical significance is set at p < 0.05. Results: The sensitivity of CA125 in detecting endometrial cancer is 16% whereas the sensitivity of HE4 is 48% and 28 % for 70 pmol/L and 150 pmol/L cut-off respectively. The specificity of HE4 is 100% (positive predictive value = 100%, negative predictive value = 65.79% and 58.14% considering the two HE4 cut-off, respectively), whereas the CA125 specificity is 72 % (positive predictive value = 36.36%, negative predictive value = 46.15%) in detection of endometrial cancer. Conclusions: HE4 has a good sensitivity and a specificity of 100% in detection of endometrial cancer and may be useful for detecting early stage endometrial cancer. In particular the HE4 at cut-off of 70 pmol/L yields the best sensitivity and specificity.

scholarly journals Added value of diffusion-weighted MRI in assessment of pleural lesions

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Youssriah Yahia Sabri ◽  
Ikram Hamed Mahmoud ◽  
Lamis Tarek El-Gendy ◽  
Mohamed Raafat Abd El-Mageed ◽  
Sally Fouad Tadros
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Value ◽  
Added Value ◽  
Adc Value ◽  
Pleural Diseases ◽  
Conventional Mri ◽  
Malignant Lesions

Abstract Background There are many causes of pleural disease including variable benign and malignant etiologies. DWI is a non-enhanced functional MRI technique that allows qualitative and quantitative characterization of tissues based on their water molecules diffusivity. The aim of this study was to evaluate the diagnostic value of DWI-MRI in detection and characterization of pleural diseases and its capability in differentiating benign from malignant pleural lesions. Results Conventional MRI was able to discriminate benign from malignant lesions by using morphological features (contour and thickness) with sensitivity 89.29%, specificity 76%, positive predictive value 89%, negative predictive value 76.92%, and accuracy 85.37%. ADC value as a quantitative parameter of DWI found that ADC values of malignant pleural diseases were significantly lower than that of benign lesions (P < 0.001). Hence, we discovered that using ADC mean value of 1.68 × 10-3 mm2/s as a cutoff value can differentiate malignant from benign pleural diseases with sensitivity 89.3%, specificity 100%, positive predictive value 100%, negative predictive value 81.2%, and accuracy 92.68% (P < 0.001). Conclusion Although DWI-MRI is unable to differentiate between malignant and benign pleural effusion, its combined morphological and functional information provide valid non-invasive method to accurately characterize pleural soft tissue diseases differentiating benign from malignant lesions with higher specificity and accuracy than conventional MRI.

Accuracy of Medical Examiner’s Assessment for Near–Real-Time Surveillance of Fatal Drug Overdoses, King County, Washington, March 2017–February 2018

2021 ◽  
pp. 003335492110084
Author(s):  
Kirsten Vannice ◽  
Julia Hood ◽  
Nicole Yarid ◽  
Meagan Kay ◽  
Richard Harruff ◽  
...  
Keyword(s):  
Public Health ◽  
Positive Predictive Value ◽  
Real Time ◽  
Negative Predictive Value ◽  
Drug Overdose ◽  
Predictive Value ◽  
Death Certificate ◽  
King County ◽  
Public Health Response ◽  
Drug Overdoses

Objectives Up-to-date information on the occurrence of drug overdose is critical to guide public health response. The objective of our study was to evaluate a near–real-time fatal drug overdose surveillance system to improve timeliness of drug overdose monitoring. Methods We analyzed data on deaths in the King County (Washington) Medical Examiner’s Office (KCMEO) jurisdiction that occurred during March 1, 2017–February 28, 2018, and that had routine toxicology test results. Medical examiners (MEs) classified probable drug overdoses on the basis of information obtained through the death investigation and autopsy. We calculated sensitivity, positive predictive value, specificity, and negative predictive value of MEs’ classification by using the final death certificate as the gold standard. Results KCMEO investigated 2480 deaths; 1389 underwent routine toxicology testing, and 361 were toxicologically confirmed drug overdoses from opioid, stimulant, or euphoric drugs. Sensitivity of the probable overdose classification was 83%, positive predictive value was 89%, specificity was 96%, and negative predictive value was 94%. Probable overdoses were classified a median of 1 day after the event, whereas the final death certificate confirming an overdose was received by KCMEO an average of 63 days after the event. Conclusions King County MEs’ probable overdose classification provides a near–real-time indicator of fatal drug overdoses, which can guide rapid local public health responses to the drug overdose epidemic.

scholarly journals Role of annexin A2 and osteopontin for early diagnosis of hepatocellular carcinoma in hepatitis C virus patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Abd El-Fattah F. Hanno ◽  
Fatma M. Abd El-Aziz ◽  
Akram A. Deghady ◽  
Ehab H. El-Kholy ◽  
Aborawy I. Aborawy
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Early Diagnosis ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Chronic Hepatitis C ◽  
Predictive Value ◽  
Annexin A2 ◽  
Alpha Fetoprotein

Abstract Background Liver cancer is the fifth most common cancer and the second most frequent cause of cancer-related death globally. Early stages of hepatocellular carcinoma (0&A) can be treated with curative procedures. The aim of this work was to evaluate the role of annexin A2 and osteopontin for early diagnosis of hepatocellular carcinoma in hepatitis C virus patients. Methods The study was carried out on 80 patients classified into two groups. Group A had 40 chronic hepatitis C patients without hepatocellular carcinoma, while group B had 40 chronic hepatitis C patients with early hepatocellular carcinoma (stages; 0&A). All patients were subjected to thorough history taking, clinical examination, liver function tests, renal function tests, serum alpha-fetoprotein, serum osteopontin, and serum annexin A2. Results Serum alpha-fetoprotein was found to be statistically significantly higher in patients with the hepatocellular carcinoma group than the chronic hepatitis C group. The ROC curve for alpha-fetoprotein for detection of HCC was significant, its diagnostic performance was 0.818* (p < 0.001*), and the cutoff point for predicting the probability for HCC was 6.0 (ng/ml) with sensitivity of 77.50%, specificity of 82.50%, positive predictive value of 81.60%, negative predictive value of 78.6%, and accuracy of 80%. Serum osteopontin was found to be statistically significantly higher in patients from the hepatocellular carcinoma group than the chronic hepatitis C group. The ROC curve for osteopontin was significant, its diagnostic performance was 0.739* (p < 0.001*), the cutoff point was 13.2 (ng/ml) with sensitivity of 65.0%, specificity of 90.0%, positive predictive value of 86.70%, negative predictive value of 72.0%, and accuracy of 77.0%. Serum annexin A2 was found to be statistically significantly higher in patients from the hepatocellular carcinoma group than the chronic hepatitis C group. The ROC curve for annexin A2 was significant, its diagnostic performance was 0.927* (p < 0.001*), the cutoff point was 10.1(ng/ml) with sensitivity of 85.0%, specificity of 85.0%, positive predictive value of 85.0%, negative predictive value of 85.0%, and accuracy of 85.0%. Conclusions Osteopontin had better specificity but lower sensitivity than serum alpha-fetoprotein for early diagnosis of hepatocellular carcinoma. Annexin A2 had better diagnostic sensitivity and specificity than alpha-fetoprotein for early diagnosis of hepatocellular carcinoma.

scholarly journals POS0706 PERFORMANCES OF DIFFERENT CLASSIFICATION CRITERIA FOR SYSTEMIC LUPUS ERYTHEMATOSUS IN A SINGLE CENTER COHORT FROM TURKEY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 602.1-603
Author(s):  
E. S. Torun ◽  
E. Bektaş ◽  
F. Kemik ◽  
M. Bektaş ◽  
C. Cetin ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Antiphospholipid Syndrome ◽  
Lupus Erythematosus ◽  
Predictive Value ◽  
Sjogren's Syndrome ◽  
Classification Criteria ◽  
Systemic Lupus ◽  
Acr Criteria

Background:Recently developed EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) have important differences compared to the 2012 Systemic Lupus International Collaborating Clinics (SLICC) SLE classification criteria and the revised 1997 American College of Rheumatology (ACR) criteria: The obligatory entry criterion of antinuclear antibody (ANA) positivity is introduced and a “weighted” approach is used1. Sensitivity and specificity of these three criteria have been debated and may vary in different populations and clinical settings.Objectives:We aim to compare the performances of three criteria sets/rules in a large cohort of patients and relevant diseased controls from a reference center with dedicated clinics for SLE and other autoimmune/inflammatory connective tissue diseases from Turkey.Methods:We reviewed the medical records of SLE patients and diseased controls for clinical and laboratory features relevant to all sets of criteria. Criteria sets/rules were analysed based on sensitivity, positive predictive value, specificity and negative predictive value, using clinical diagnosis with at least 6 months of follow-up as the gold standard. A subgroup analysis was performed in ANA positive patients for both SLE patients and diseased controls. SLE patients that did not fulfil 2012 SLICC criteria and 2019 EULAR/ACR criteria and diseased controls that fulfilled these criteria were evaluated.Results:A total of 392 SLE patients and 294 non-SLE diseased controls (48 undifferentiated connective tissue disease, 51 Sjögren’s syndrome, 43 idiopathic inflammatory myopathy, 50 systemic sclerosis, 52 primary antiphospholipid syndrome, 15 rheumatoid arthritis, 15 psoriatic arthritis and 20 ANCA associated vasculitis) were included into the study. Hundred and fourteen patients (16.6%) were ANA negative.Sensitivity was more than 90% for 2012 SLICC criteria and 2019 EULAR/ACR criteria and positive predictive value was more than 90% for all three criteria (Table 1). Specificity was the highest for 1997 ACR criteria. Negative predictive value was 76.9% for ACR criteria, 88.4% for SLICC criteria and 91.7% for EULAR/ACR criteria.In only ANA positive patients, sensitivity was 79.6% for 1997 ACR criteria, 92.2% for 2012 SLICC criteria and 96.1% for 2019 EULAR/ACR criteria. Specificity was 92.6% for ACR criteria, 87.8% for SLICC criteria 85.2% for EULAR/ACR criteria.Eleven clinically diagnosed SLE patients had insufficient number of items for both 2012 SLICC and 2019 EULAR/ACR criteria. Both criteria were fulfilled by 16 diseased controls: 9 with Sjögren’s syndrome, 5 with antiphospholipid syndrome, one with dermatomyositis and one with systemic sclerosis.Table 1.Sensitivity, positive predictive value, specificity and negative predictive value of 1997 ACR, 2012 SLICC and 2019 EULAR/ACR classification criteriaSLE (+)SLE (-)Sensitivity (%)Positive Predictive Value (%)Specificity (%)Negative Predictive Value (%)1997 ACR(+) 308(-) 841527978.695.494.976.92012 SLICC(+) 357(-) 352626891.193.291.288.42019 EULAR/ACR(+) 368(-) 242826693.892.990.591.7Conclusion:In this cohort, although all three criteria have sufficient specificity, sensitivity and negative predictive value of 1997 ACR criteria are the lowest. Overall, 2019 EULAR/ACR and 2012 SLICC criteria have a comparable performance, but if only ANA positive cases and controls are analysed, the specificity of both criteria decrease to less than 90%. Some SLE patients with a clinical diagnosis lacked sufficient number of criteria. Mostly, patients with Sjögren’s syndrome or antiphospholipid syndrome are prone to misclassification by both recent criteria.References:[1]Aringer M, Costenbader K, Daikh D, et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann Rheum Dis 2019;78:1151-1159.Disclosure of Interests:None declared

scholarly journals Clinicopathological features and prognostic significance of CTNNB1 mutation in low-grade, early-stage endometrial endometrioid carcinoma

2021 ◽  
Author(s):  
Ignacio Ruz-Caracuel ◽  
Álvaro López-Janeiro ◽  
Victoria Heredia-Soto ◽  
Jorge L. Ramón-Patino ◽  
Laura Yébenes ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Mismatch Repair ◽  
Predictive Value ◽  
Early Stage ◽  
Disease Free Survival ◽  
Low Grade ◽  
Beta Catenin ◽  
Free Survival ◽  
Disease Free ◽  
Risk Of Relapse

AbstractLow-grade and early-stage endometrioid endometrial carcinomas (EECs) have an overall good prognosis but biomarkers identifying patients at risk of relapse are still lacking. Recently, CTNNB1 exon 3 mutation has been identified as a potential risk factor of recurrence in these patients. We evaluate the prognostic value of CTNNB1 mutation in a single-centre cohort of 218 low-grade, early-stage EECs, and the correlation with beta-catenin and LEF1 immunohistochemistry as candidate surrogate markers. CTNNB1 exon 3 hotspot mutations were evaluated by Sanger sequencing. Immunohistochemical staining of mismatch repair proteins (MLH1, PMS2, MSH2, and MSH6), p53, beta-catenin, and LEF1 was performed in representative tissue microarrays. Tumours were also reviewed for mucinous and squamous differentiation, and MELF pattern. Nineteen (8.7%) tumours harboured a mutation in CTNNB1 exon 3. Nuclear beta-catenin and LEF1 were significantly associated with CTNNB1 mutation, showing nuclear beta-catenin a better specificity and positive predictive value for CTNNB1 mutation. Tumours with CTNNB1 exon 3 mutation were associated with reduced disease-free survival (p = 0.010), but no impact on overall survival was found (p = 0.807). The risk of relapse in tumours with CTNNB1 exon 3 mutation was independent of FIGO stage, tumour grade, mismatch repair protein expression, or the presence of lymphovascular space invasion. CTNNB1 exon 3 mutation has a negative impact on disease-free survival in low-grade, early-stage EECs. Nuclear beta-catenin shows a higher positive predictive value than LEF1 for CTNNB1 exon 3 mutation in these tumours. Graphical abstract

Influence of lesion location on the accuracy of CT derived FFR: head-to-head comparison with invasive FFR

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Moshage ◽  
S Smolka ◽  
S Achenbach ◽  
F Ammon ◽  
P Ferstl ◽  
...  
Keyword(s):  
Coronary Angiography ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Invasive Coronary Angiography ◽  
Roc Curve Analysis ◽  
Jude Medical ◽  
Lesion Location ◽  
Distal Vessel

Abstract Background The accuracy of CT-derived FFR (FFRCT) has been repeatedly reported. However, the influence of lesion location on accuracy is unknown. Therefore, we evaluated the diagnostic accuracy of FFRCT to detect lesion-specific ischemia and determined the influence of lesion location (proximal vs. distal vessel segments) compared to invasively measured FFR in patients with suspected CAD. Methods A total of 136 vessels in which “Dual-Source”-CT coronary angiography had been performed due to suspected CAD and who were further referred for invasive coronary angiography with invasive FFR measurement within three months of the index CT examination were retrospectively identified and screened for inclusion in this analysis. Patients with either left main coronary artery stenoses, bifurcation or ostial stenoses were excluded. Invasive FFR was measured using a pressure wire (CERTUS®, St. Jude Medical, Minnesota, USA or Verrata®, Volcano, San Diego, USA). FFRCT was calculated using an on-site prototype (cFFR Version 3.0, Siemens Healthineers, Forchheim, Germany). All vessels were analyzed by an experienced observer blinded to the results of invasive FFR. Stenoses with invasively measured FFR ≤0.80 were classified as hemodynamically significant. We evaluated the diagnostic accuracy of FFRCT in proximal vs. non-proximal vessel segments. Proximal lesions included stenoses located in segment one, six, eleven and twelve. All other stenoses were categorized as distal lesions. Results Out of 136 coronary stenoses, 47 (35%) were located in proximal segments and 89 (65%) lesions were located in distal segments. Compared to invasive FFR, the sensitivity of FFRCT to correctly identify/exclude hemodynamically significant stenoses in proximal vessel segments was 93% (95% CI: 68–99.8%) and the specificity was 100% (95% CI: 89–100%), compared to a sensitivity of 72% (95% CI: 46.5–90%) and a specificity of 87% (95% CI: 77–94%) for FFRCT in distal lesions. The positive predictive value was 100% and the negative predictive value was 97% (95% CI: 82.8–99.5%) compared to a positive predictive value of 59% (95% CI: 42–93.9%) and a negative predictive value of 93% (95% CI: 85.4–96.3%) for proximal vs. distal vessel segment, respectively. This corresponds to an accuracy of 98% vs. 84%, respectively (p=0.02). ROC-Curve analysis showed a slightly higher – albeit non-significant – area under the curve for FFRCT to detect hemodynamic relevance in proximal lesions compared to distal lesions (AUC 0.95, p&lt;0.001 vs. AUC: 0.86, p&lt;0.001, respectively, p=0.2). Conclusion FFRCT obtained using an on-site prototype shows overall a high diagnostic accuracy for detecting lesions causing ischemia as compared to invasive FFR with a trend towards better diagnostic performance in proximal vessel segments. Funding Acknowledgement Type of funding source: None

Sign in / Sign up

Export Citation Format

Share Document