scholarly journals The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma

2021 ◽  
Author(s):  
Hui Tan ◽  
Hui Xu ◽  
Nan Yu ◽  
Yong Yu ◽  
Haifeng Duan ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Reading Time ◽  
Chest Trauma ◽  
Diagnostic System ◽  
Rib Fracture ◽  
Diagnostic Sensitivity ◽  
Reference Standard ◽  
Diagnostic Confidence

Abstract Purpose To evaluate the value of a deep learning-based computer-aided diagnostic system (DL-CAD) in improving the diagnostic performance of acute rib fractures in patients with chest trauma. Methods CT images of 214 patients with acute blunt chest trauma were retrospectively analyzed by two interns and two attending radiologists independently firstly and then with the assistance of a DL-CAD one month later, in a blinded and randomized manner. The consensus diagnosis of fib fracture by another two senior thoracic radiologists was regarded as reference standard. The rib fracture diagnostic sensitivity, specificity, positive predictive value, diagnostic confidence and mean reading time with and without DL-CAD were calculated and compared. Results There were 680 rib fracture lesions confirmed as reference standard among all patients. The diagnostic sensitivity and positive predictive value of interns were significantly improved from (68.82%, 84.50%) to (91.76%, 93.17%) with the assistance of DL-CAD, respectively. Diagnostic sensitivity and positive predictive value of interns assisted by DL-CAD were comparative to those of attendings aided by DL-CAD (94.56%, 86.47%) or not aided (95.67%, 93.83%), respectively. In addition, when radiologists were assisted by DL-CAD, the mean reading time was significantly reduced and diagnostic confidence was significantly enhanced. Conclusions DL-CAD improves the diagnostic performance of acute rib fracture in chest trauma patients, which increases the diagnostic confidence, sensitivity and positive predictive value for radiologists. DC-CAD can advance the diagnostic consistency of radiologists with different experiences.

scholarly journals Semiautomated procedures for evaluation of carbohydrate-deficient transferrin in the diagnosis of alcohol abuse

1997 ◽  
Vol 43 (6) ◽  
pp. 983-989 ◽  
Author(s):  
Pamela Bean ◽  
Karsten Liegmann ◽  
Trond Løvli ◽  
Christina Westby ◽  
Erling Sundrehagen
Keyword(s):  
Alcohol Abuse ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Biological Marker ◽  
Cost Effective ◽  
Carbohydrate Deficient Transferrin ◽  
Laser Densitometry ◽  
Turbidimetric Immunoassay ◽  
Better Than

Abstract Carbohydrate-deficient transferrin (CDT) may now be the most valuable biological marker for diagnosis of alcohol abuse. We compared the diagnostic performance of two new CDT tests, Axis %CDT turbidimetric immunoassay (TIA) and Axis %CDT HPLC, against Specialty Laboratories’ isoelectric focusing/immunoblotting/laser densitometry (IEF/IB/LD). Both Axis tests include one-half the concentration of trisialotransferrin isoforms in their CDT quantitation schemes. Considering an alcohol abuse prevalence of 7%, Axis %CDT TIA shows a sensitivity of 87% at 98% specificity and a positive predictive value (PPV) of 0.75; %CDT HPLC shows a sensitivity of 87% at 100% specificity for a PPV of 1, and the IEF/IB/LD shows 81% sensitivity at 94% specificity for a PPV of 0.5. All three CDT tests show the same negative predictive value (0.98). Both Axis procedures perform better than IEF/IB/LD in the diagnosis of alcohol abuse; %CDT TIA is available in several semiautomated, cost-effective formats.

1268A comparison between the diagnostic performance of quantitative flow ratio and non-invasive imaging modalities for diagnosing myocardial ischemia defined by FFR, a PACIFIC-trial interim analysis

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
P A A Van Diemen ◽  
R S Driessen ◽  
R A Kooistra ◽  
W J Stuijfzand ◽  
P G Raijmakers ◽  
...  
Keyword(s):  
Coronary Angiography ◽  
Myocardial Ischemia ◽  
Positive Predictive Value ◽  
Flow Velocity ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Emission Tomography ◽  
Flow Ratio ◽  
Quantitative Flow Ratio ◽  
Quantitative Flow

Abstract Background Quantitative flow ratio (QFR) uses fast computational algorithms based on 3-dimensional quantitative coronary angiography and estimation of contrast flow velocity during invasive coronary angiography (ICA) to obtain QFR values equivalent to fractional flow reserve (FFR). Objective To compare the diagnostic performance of QFR with coronary computed tomography angiography (CCTA), single-photon emission tomography (SPECT), and positron emission tomography (PET) for diagnosing myocardial ischemia defined by FFR. Method QFR computation was attempted in 109 patients (286 vessels without a subtotal/total lesion) of the 208 patients included in the PACIFIC-trial. Patients underwent 256-slice CCTA, Tetrofosmin SPECT, and [15O]H2O PET prior to ICA in conjunction with 3 vessel FFR measurements. ICA images were obtained without the use of a dedicated QFR acquistion protocol. QFR was calculated using a fixed empiric hyperemic flow velocity (fQFR) as well as using a patient specific flow velocity based on contrast passage through the coronary (cQFR). All analysis were performed on a per vessel level. Results Fixed QFR computation succeeded in 152 (53%) vessels while cQFR analysis was successful in 140 (49%) vessels. A good correlation between FFR and fQFR/cQFR was observed (R=0.774, p<0.001/R=0.790, p<0.001). The diagnostic performance in terms of sensitivity, specificity, negative predictive value, positive predictive value, and accuracy is presented in table 1. In total, 133 vessels with matched FFR, fQFR, cQFR, CCTA, SPECT, and PET results were available for the comparative C-statistic analysis, figure 1. The diagnostic performance of fQFR and cQFR was comparable (p=0.451) and superior to CCTA (p=0.004/p=0.003), SPECT (p<0.001/p<0.001), and PET (p=0.008/p=0.006), figure 1. CCTA, and PET performed alike (p=0.568) and outperformed SPECT (p=0.023, p=0.002). Table 1 % (95% Confidence Interval) fQFR n=152 cQFR (n=140) CCTA (n=152) SPECT (n=150) PET (n=149) Sensitivity 76 (59–89) 71 (53–86) 70 (51–84) 30 (16–49) 76 (58–89) Specificity 94 (88–98) 93 (86–97) 73 (64–81) 96 (90–99) 80 (72–87) Negative Predictive Value 93 (88–96) 92 (86–95) 90 (84–94) 83 (79–86) 92 (86–96) Positive Predictive Value 79 (64–89) 74 (59–85) 42 (33–51) 67 (42–84) 52 (42–62) Accuracy 90 (84–94) 88 (81–93) 72 (65–79) 81 (74–87) 79 (72–85) Figure 1. Conclusion Fixed QFR and cQFR correlate well with FFR with a high diagnostic accuracy as result. QFR outperformed CCTA, SPECT, and PET for the diagnosis of myocardial ischemia on a per vessel basis with the important footnote that fQFR and cQFR could only be computed in 53%, and 49% of the vessels.

Towards malaria microscopy at the point-of-contact: an assessment of the diagnostic performance of the Newton Nm1 microscope in Uganda

Parasitology ◽  
2014 ◽  
Vol 141 (14) ◽  
pp. 1819-1825 ◽  
Author(s):  
J. RUSSELL STOTHARD ◽  
BETTY NABATTE ◽  
JOSE C. SOUSA-FIGUEIREDO ◽  
NARCIS B. KABATEREINE
Keyword(s):  
Confidence Interval ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Blood Cells ◽  
Diagnostic Performance ◽  
White Blood Cells ◽  
Sub Saharan Africa ◽  
Resource Poor ◽  
Sub Saharan ◽  
Malaria Microscopy

SUMMARYMalaria microscopy in sub-Saharan Africa is often restricted by access to light microscopes. To address this gap, a novel portable inverted monocular microscope, the Newton Nm1, was designed and is now commercially available. Its diagnostic performance was assessed in a blinded-slide trial at ×1000 (oil) of Giemsa-stained thick blood films against a conventional microscope as undertaken by four Ugandan Ministry of Health technicians. With the Newton Nm1, diagnostic performance was: sensitivity 93·5% (95% confidence interval (CI) 78·6–99·2%), specificity 100·0% (95% CI 82·4–100·0%), positive predictive value 100·0% (95% CI 88·1–100·0%) and negative predictive value 90·5% (95% CI 69·6–98·8%). Discordance was due to a systematic error underestimating parasitaemia by ~45%; when counting Plasmodium parasites against 200 white blood cells, blood films with low parasitaemia (i.e. <100 μL−1 of blood) could be overlooked and misclassified. By contrast, specificity was excellent with no false positives encountered. Whilst proven useful, especially in resource-poor environments, it is still unclear how we can ensure the uptake of the Newton Nm1 within sub-Saharan Africa.

“Category 4A” microcalcifications: how should this subcategory be applied to microcalcifications seen on mammography?

2017 ◽  
Vol 59 (2) ◽  
pp. 147-153 ◽  
Author(s):  
Jihee Kim ◽  
Eun-Kyung Kim ◽  
Min Jung Kim ◽  
Hee Jung Moon ◽  
Jung Hyun Yoon
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Imaging Features ◽  
Reference Standard ◽  
Predictive Values ◽  
Guided Biopsy

Background The BI-RADS provides descriptors for microcalcifications based on morphology and distribution. However, the descriptor for category 4A microcalcifications is not specified in the 5th BI-RADS nor in the prior editions. Purpose To investigate how the category 4A assessment is applied to suspicious microcalcifications, and the appropriate descriptors for category 4A assessment for microcalcifications seen on mammography. Material and Methods From June 2008 to November 2011, 296 women with 305 suspicious microcalcifications that were assessed as BI-RADS category 4A, who underwent imaging-guided biopsy or surgery, were included. Mammograms were reviewed and imaging features were analyzed according to the morphology and distribution descriptors of the 5th edition of BI-RADS. Pathological results were considered as the reference standard. Positive predictive values were calculated and compared between morphology and distribution descriptors. Results The overall positive predictive value (PPV) of category 4A microcalcifications was 17.7% (54/305). Most common descriptors for category 4A microcalcifications were amorphous morphology (73.1%) and grouped distribution (71.1%). PPVs of individual morphology descriptors were as follows: amorphous = 7.2%; coarse heterogeneous = 12.8%; fine pleomorphic or fine linear/fine linear branching = 91.4% ( P < 0.001). PPVs of distribution descriptors were as follows: regional = 13.2%; grouped = 16.1%; linear/segmental = 54.5% ( P < 0.001). For morphology and distribution descriptors combinations, PPVs for amorphous/regional and amorphous/grouped microcalcifications were 6.8% and 6.9%, while PPVs of other combinations were higher than 10%, respectively. Conclusion Common descriptors used in category 4A assessment for microcalcifications are amorphous morphology and grouped distribution. PPV of amorphous/regional and amorphous/grouped microcalcifications were suitable for category 4A assessment.

scholarly journals DIAGNOSTIC ACCURACY OF ULTRASOUND IN THE DETECTION OF PNEUMOTHORAX IN CHEST TRAUMA PATIENTS WITH CT AS THE GOLD STANDARD

2021 ◽  
Vol 5 (7) ◽  
Author(s):  
Danquale Vance Kynshikhar ◽  
Chaman Lal Kaushal ◽  
Ashwani Tomar ◽  
Neeti Aggarwal
Keyword(s):  
Emergency Department ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Gold Standard ◽  
Chest Trauma ◽  
Accurate Diagnosis ◽  
Trauma Patients ◽  
Chest Ultrasound

Background: To study the diagnostic accuracy of ultrasound in the detection of pneumothorax in chest trauma patients with CT as the Gold Standard Methods: The present study was conducted from 31th July 2018 to 30th July 2019. A total of 36 patients were enrolled in the study. Results: By chest ultrasound, pneumothorax was detected in 15 of 24 patients. The sensitivity of chest ultrasound for the diagnosis of pneumothorax was 62.5%, specificity was 100%, positive predictive value (PPV) was 100%, negative predictive value (NPV) was 54.14% and accuracy was 75%. Conclusion: Chest ultrasound can play an important role in the emergency department aiding a physician for bedside rapid and accurate diagnosis of pneumothorax without interruption in the resuscitation process and without transferring the patient to the radiology section. Keywords: Ultrasound, CT, Pneumothorax

scholarly journals Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease

2020 ◽  
Vol 93 (1113) ◽  
pp. 20191028 ◽  
Author(s):  
Meng Chen ◽  
Ximing Wang ◽  
Guangyu Hao ◽  
Xujie Cheng ◽  
Chune Ma ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Operating Characteristics ◽  
Stenosis Detection ◽  
Artery Disease

Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). Results: In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). Conclusion: The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. Advances in knowledge: The DL technology has valuable prospect with the diagnostic ability to detect CAD.

scholarly journals Static CT myocardial perfusion imaging: image quality, artifacts including distribution and diagnostic performance compared to 82Rb PET

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
João R. Inácio ◽  
Sriraag Balaji Srinivasan ◽  
Terrence D. Ruddy ◽  
Robert A. deKemp ◽  
Frank Rybicki ◽  
...  
Keyword(s):  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
High Sensitivity ◽  
Reference Standard ◽  
Pharmacological Stress ◽  
Perfusion Defects ◽  
Perfusion Deficits

Abstract Background Rubidium-82 positron emission tomography (82Rb PET) MPI is considered a noninvasive reference standard for the assessment of myocardial perfusion in coronary artery disease (CAD) patients. Our main goal was to compare the diagnostic performance of static rest/ vasodilator stress CT myocardial perfusion imaging (CT-MPI) to stress/ rest 82Rb PET-MPI for the identification of myocardial ischemia. Methods Forty-four patients with suspected or diagnosed CAD underwent both static CT-MPI and 82Rb PET-MPI at rest and during pharmacological stress. The extent and severity of perfusion defects on PET-MPI were assessed to obtain summed stress score, summed rest score, and summed difference score. The extent and severity of perfusion defects on CT-MPI was visually assessed using the same grading scale. CT-MPI was compared with PET-MPI as the gold standard on a per-territory and a per-patient basis. Results On a per-patient basis, there was moderate agreement between CT-MPI and PET-MPI with a weighted 0.49 for detection of stress induced perfusion abnormalities. Using PET-MPI as a reference, static CT-MPI had 89% sensitivity (SS), 58% specificity (SP), 71% accuracy (AC), 88% negative predictive value (NPV), and 59% positive predictive value (PPV) to diagnose stress-rest perfusion deficits on a per-patient basis. On a per-territory analysis, CT-MPI had 73% SS, 65% SP, 67% AC, 90.8% NPV, and 34% PPV to diagnose perfusion deficits. Conclusions CT-MPI has high sensitivity and good overall accuracy for the diagnosis of functionally significant CAD using 82Rb PET-MPI as the reference standard. CT-MPI may play an important role in assessing the functional significance of CAD especially in combination with CCTA.

scholarly journals Diagnostic Accuracy of a MR Protocol Acquired with and without Endorectal Coil for Detection of Prostate Cancer: A Multicenter Study

2018 ◽  
Vol 12 (2) ◽  
pp. 88-96 ◽  
Author(s):  
Borna K. Barth ◽  
Niels J. Rupp ◽  
Alexander Cornelius ◽  
Daniel Nanz ◽  
Rainer Grobholz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Reference Standard ◽  
Resonance Imaging ◽  
Index Lesion ◽  
Multiparametric Magnetic Resonance Imaging ◽  
Endorectal Coil ◽  
Whole Mount

Introduction: The purpose of this study was to compare diagnostic accuracy of a prostate multiparametric magnetic resonance imaging (mpMRI) protocol for detection of prostate cancer between images acquired with and without en-dorectal coil (ERC). Materials: This study was approved by the regional ethics committee. Between 2014 and 2015, 33 patients (median age 51.3 years; range 42.1-77.3 years) who underwent prostate-MRI at 3T scanners at 2 different institutions, acquired with (mpMRIERC) and without (mpMRIPPA) ERC and who received radical prostatectomy, were included in this retrospective study. Two expert readers (R1, R2) attributed a PI-RADS version 2 score for the most suspect (i. e. index) lesion for mpMRIPPA and mpMRIERC. Sensitivity and positive predictive value for detection of index lesions were assessed using 2 × 2 contingency tables. Differences between groups were tested using the McNemar test. Whole-mount histopathology served as reference standard. Results: On a quadrant-basis cumulative sensitivity ranged between 0.61-0.67 and 0.76-0.88 for mpMRIPPA and mpMRIERC protocols, respectively (p > 0.05). Cumulative positive predictive value ranged between 0.80-0.81 and 0.89-0.91 for mpMRIPPA and mpMRIERC protocols, respectively. The differences were not statistically significant for R1 (p = 0.267) or R2 (p = 0.508). Conclusion: Our results suggest that there may be no significant differences for detection of prostate cancer between images acquired with and without an ERC.

scholarly journals CT Scan in the Prediction of Lymph Node Involvement in Ovarian Cancer – a Retrospective Analysis of a Tertiary Gyneco-Oncological Unit

2020 ◽  
Vol 80 (05) ◽  
pp. 518-525 ◽  
Author(s):  
Peter Widschwendter ◽  
Alexandra Blersch ◽  
Thomas W. P. Friedl ◽  
Wolfgang Janni ◽  
Christopher Kloth ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Lymph Node ◽  
Positive Predictive Value ◽  
Lymph Nodes ◽  
Negative Predictive Value ◽  
Computer Tomography ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Lymph Node Involvement ◽  
Lymph Node Status

Abstract Background The prognostic value of lymph node removal in ovarian cancer varies depending on the tumor stage. While in the advanced stage the removal of clinically normal lymph nodes does not improve the prognosis, this is still unclear in the early stages. Evaluation of the lymph nodes based on preoperative imaging influences the surgical procedure. Methods This retrospective analysis was performed by analyzing data from 114 patients with ovarian cancer, treated in our university hospital in the years 2000 – 2012. Diagnostic performance of imaging by computer tomography with respect to the correct prediction of lymph node status was analyzed in terms of sensitivity, specificity, positive predictive value and negative predictive value. Results Imaging by computer tomography showed a rather limited diagnostic performance with regard to the detection of lymph node metastases in ovarian cancer, with a sensitivity of 40.7%, a specificity of 89.1%, a positive predictive value of 80.0%, and a negative predictive value of 58.3%. A separate analysis for pelvic and paraaortic lymph node involvement showed a better diagnostic performance of computer tomography for the detection of positive paraaortic lymph nodes (41.2, 93.1, 84.0, and 64.3% for sensitivity, specificity, positive predictive value and negative predictive value, respectively) as compared to the detection of positive pelvic lymph nodes (25.6, 91.8, 62.5, and 69.8%). Conclusion The preoperative prediction of lymph node status by computer tomography is limited. A decision for or against lymphadenectomy should not be made solely on the basis of this approach.

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