scholarly journals COVID-19 automatic diagnosis with CT images using the novel Transformer architecture

2021 ◽  
Author(s):  
Gabriel Sousa Silva Costa ◽  
Anselmo C. Paiva ◽  
Geraldo Braz Júnior ◽  
Marco Melo Ferreira
Keyword(s):  
Intelligent System ◽  
State Of The Art ◽  
False Negative ◽  
False Negative Rate ◽  
Ct Images ◽  
The Novel ◽  
Chain Reaction ◽  
High False Negative Rate ◽  
Polymerase Chain ◽  
Low Sensitivity

Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the first tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modified vision transformer, reaching 96.00% accuracy.

Pathologic Assessment of Computerized Tomography Accuracy for the Evaluation of the Laryngeal Cartilaginous Frame Work in Laryngopharyngeal Carcinomas

1989 ◽  
Vol 75 (2) ◽  
pp. 156-162 ◽  
Author(s):  
Sandro Sulfaro ◽  
Francesco Querin ◽  
Luigi Barzan ◽  
Mario Lutman ◽  
Roberto Comoretto ◽  
...  
Keyword(s):  
Computerized Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Cartilage Destruction ◽  
High False Negative Rate ◽  
Positive Rate ◽  
Pathologic Assessment ◽  
Low Sensitivity

Sixty-six whole-organ sectioned laryngopharyngectomy specimens removed for cancer during a seven-year period were uniformly examined to determine the accuracy of preoperative high resolution computerized tomography (CT) for detection of cartilaginous involvement. Our results indicate that CT has a high overall specificity (88.2%) but a low sensitivity (47.1 %); we observed a high false-negative rate (26.5%) and a fairly low false-positive rate (5.9%). Massive cartilage destruction was easily assessed by CT, whereas both small macroscopic and microscopic neoplastic foci of cartilaginous invasion were missed on CT scans. Moreover, false-positive cases were mainly due to proximity of the tumor to the cartilage. Clinical implications of these results are discussed.

scholarly journals Novel scotoma detection method using time required for fixation to the random targets

2021 ◽  
Author(s):  
Nobuyuki Takahashi ◽  
Shozo Saeki ◽  
Minoru Kawahara ◽  
Hirohisa Aman ◽  
Eri Nakano ◽  
...  
Keyword(s):  
Detection Method ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
The Novel ◽  
Point Distribution ◽  
Humphrey Perimetry ◽  
Measurement Results ◽  
Low Sensitivity ◽  
Time Required

We developed a novel scotoma detection system using time required for fixation to the random targets, or the "eye-guided scotoma detection method". In order to verify the "eye-guided scotoma detection method", we measured 78 eyes of 40 subjects, and examined the measurement results in comparison with the results of measurement by Humphrey perimetry. The results were as follows: (1) Mariotte scotomas were detected in 100\% of the eyes tested; (2) The false-negative rate (the percentage of cases where a scotoma was evaluated as a non-scotoma) was less than 10\%; (3) The positive point distribution in the low-sensitivity eyes was well matched. These findings suggested that the novel scotoma detection method in the current study will pave the way for the realization of mass screening to detect pathological scotoma earlier.

scholarly journals Avoiding COVID-19 Hospital Outbreaks: RT-PCR Swab and Clinical Assessment

2021 ◽  
Author(s):  
Luca Allievi ◽  
Amedeo Bongarzoni ◽  
Guido Tassinario ◽  
Stefano Carugo
Keyword(s):  
Clinical Assessment ◽  
False Negative ◽  
False Negative Rate ◽  
High Specificity ◽  
Diagnostic Process ◽  
Rt Pcr ◽  
Negative Results ◽  
High False Negative Rate ◽  
Degree Of Confidence ◽  
Low Sensitivity

Nasopharyngeal RT-PCR swab test for COVID-19 diagnosis has a high specificity but also a low sensitivity. The high false-negative rate and the overconfidence in negative results sometimes lead to hospital outbreaks. Therefore, we recommend always integrating the clinical assessment in the diagnostic process, mostly after the test, to determine what degree of confidence can be attributed to a negative result.

High False-Negative Rate of HER2 Quantitative Reverse Transcription Polymerase Chain Reaction of the Oncotype DX Test: An Independent Quality Assurance Study

2011 ◽  
Vol 29 (32) ◽  
pp. 4279-4285 ◽  
Author(s):  
David J. Dabbs ◽  
Molly E. Klein ◽  
Syed K. Mohsin ◽  
Raymond R. Tubbs ◽  
Yongli Shuai ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Quality Assurance ◽  
Reverse Transcription ◽  
False Negative ◽  
False Negative Rate ◽  
Oncotype Dx ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Negative Rate ◽  
Polymerase Chain

Purpose HER2 (ERBB2) status is an important prognostic and predictive marker in breast carcinoma. In recent years, Genomic Health (GHI), purveyors of the Oncotype DX test, has been separately reporting HER2 by reverse transcription polymerase chain reaction (RT-PCR) to oncologists. Because of the lack of independent evaluation, this quality assurance study was undertaken to define the concordance rate between immunohistochemistry (IHC)/fluorescent in situ hybridization (FISH) and GHI RT-PCR HER2 assay. Methods All patients at three participating laboratories (Magee-Womens Hospital [Pittsburgh, PA], Cleveland Clinic [Cleveland, OH], and Riverside Methodist Hospital [Columbus, OH]) with available HER2 RT-PCR results from GHI were included in this study. All IHC-positive and equivocal patient cases were further evaluated and classified by FISH at respective laboratories. Results Of the total 843 patient cases, 784 (93%) were classified as negative, 36 (4%) as positive, and 23 (3%) as equivocal at the three institutions using IHC/FISH. Of the 784 negative patient cases, 779 (99%) were also classified as negative by GHI RT-PCR assay. However, all 23 equivocal patient cases were reported as negative by GHI. Of the 36 positive cases, only 10 (28%; 95% CI, 14% to 45%) were reported as positive, 12 (33%) as equivocal, and 14 (39%) as negative. Conclusion There was an unacceptable false-negative rate for HER2 status with GHI HER2 assay in this independent study. This could create confusion in the decision-making process for targeted treatment and potentially lead to mismanagement of patients with breast cancer if only GHI HER2 information is used.

scholarly journals Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction–Based SARS-CoV-2 Tests by Time Since Exposure

2020 ◽  
Vol 173 (4) ◽  
pp. 262-267 ◽  
Author(s):  
Lauren M. Kucirka ◽  
Stephen A. Lauer ◽  
Oliver Laeyendecker ◽  
Denali Boon ◽  
Justin Lessler
Keyword(s):  
Polymerase Chain Reaction ◽  
Reverse Transcriptase ◽  
False Negative ◽  
False Negative Rate ◽  
Chain Reaction ◽  
Negative Rate ◽  
Polymerase Chain

scholarly journals Deep learning for COVID-19 detection based on CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wentao Zhao ◽  
Wei Jiang ◽  
Xinguo Qiu
Keyword(s):  
State Of The Art ◽  
A Priori ◽  
Ct Images ◽  
Learning Approach ◽  
Medical Systems ◽  
Chain Reaction ◽  
Polymerase Chain Reaction Testing ◽  
Computed Tomography Images ◽  
Satisfactory Performance ◽  
Polymerase Chain

AbstractCOVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.

scholarly journals COVID19 Diagnosis using AutoML from 3D CT scans

2021 ◽  
Author(s):  
Talha Anwar
Keyword(s):  
Ct Scan ◽  
False Negative ◽  
False Negative Rate ◽  
Detailed Examination ◽  
Ct Scans ◽  
3D Ct ◽  
Chain Reaction ◽  
3D Ct Scan ◽  
Automated Machine Learning ◽  
Polymerase Chain

Coronavirus is a pandemic that affects the respiratory system causing cough, shortness of breath, and death in severe cases. Polymerase chain reaction (PCR) tests are used to diagnose coronavirus. The false-negative rate of these tests is high, so there needs a supporting method for an accurate diagnosis. CT scan provides a detailed examination of the chest to diagnose COVID but a single CT scan comprises hundreds of slices. Expert and experienced radiologists and pulmonologists can diagnose COVID from these hundreds of slices, but this is very time-consuming. So an automatic artificial intelligence (AI) based method is required to diagnose coronavirus with high accuracy. Developing this AI-based technique requires a lot of resources and time, but once it is developed, it can significantly help the clinicians. This paper used an Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis. The AutoML models are trained on 2D slices instead of 3D CT scans, and the predictions on unknown data (slices of CT scan) are aggregated to form a prediction of 3D CT scan. The aggregation process picked the most occurred case, whether COVID or non-COVID from all CT scan slices and labeled the 3D CT scan accordingly. Different thresholds are also used to label COVID or non-COVID 3D CT scans from 2D slices. The approach resulted in accuracy and F1-score of 89% and 88%, respectively. Implementation is available at github.com/talhaanwarch/mia-covid19

scholarly journals Urgent care study of the LumiraDx SARS-CoV-2 Ag Test for rapid diagnosis of COVID-19

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Jared Gresh ◽  
Harold Kisner ◽  
Brian DuChateau
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Urgent Care ◽  
Reference Laboratory ◽  
Chain Reaction ◽  
Community Based ◽  
Negative Results ◽  
Polymerase Chain ◽  
Care Study ◽  
Regional Reference

Abstract Background Testing individuals suspected of severe acute respiratory syndrome–like coronavirus 2 (SARS-CoV-2) infection is essential to reduce the spread of disease. The purpose of this retrospective study was to determine the false negativity rate of the LumiraDx SARS-CoV-2 Ag Test when utilized for testing individuals suspected of SARS-CoV-2 infection. Methods Concurrent swab samples were collected from patients suspected of SARS-CoV-2 infection by their healthcare provider within two different urgent care centers located in Easton, MA, USA and East Bridgewater, MA, USA. One swab was tested using the LumiraDx SARS-CoV-2 Ag Test. Negative results in patients considered at moderate to high risk of SARS-CoV-2 infection were confirmed at a regional reference laboratory by polymerase chain reaction (PCR) using the additional swab sample. The data included in this study was collected retrospectively as an analysis of routine clinical practice. Results From October 19, 2020 to January 3, 2021, a total of 2241 tests were performed using the LumiraDx SARS-CoV-2 Ag Test, with 549 (24.5%) testing positive and 1692 (75.5%) testing negative. A subset (800) of the samples rendering a negative LumiraDx SARS-CoV-2 Ag Test was also tested using a PCR-based test for SARS-CoV-2. Of this subset, 770 (96.3%) tested negative, and 30 (3.8%) tested positive. Negative results obtained with the LumiraDx SARS-CoV-2 Ag test demonstrated 96.3% agreement with PCR-based tests (CI 95%, 94.7–97.4%). A cycle threshold (CT) was available for 17 of the 30 specimens that yielded discordant results, with an average CT value of 31.2, an SD of 3.0, and a range of 25.2–36.3. CT was > 30.0 in 11/17 specimens (64.7%). Conclusions This study demonstrates that the LumiraDx SARS-CoV-2 Ag Test had a low false-negative rate of 3.8% when used in a community-based setting.

scholarly journals COVID-19 Detection From Chest Radiographs Using Machine Learning and Convolutional Neural Networks

2020 ◽  
Author(s):  
Andrew C. Li ◽  
David T. Lee ◽  
Kristoff K. Misquitta ◽  
Kaiji Uno ◽  
Sasha Wald
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
False Negative ◽  
False Negative Rate ◽  
Turnaround Time ◽  
Chest Radiographs ◽  
Machine Learning Model ◽  
High False Negative Rate ◽  
Machine Learning Approach ◽  
Polymerase Chain

Accurate and efficient diagnosis of potential COVID-19 patients is vital in the fight against the current pandemic. However, even the gold-standard COVID-19 test, reverse transcription polymerase chain reaction, suffers from a high false negative rate and a turnaround time of up to one week, preventing the infected from accessing the timely care they require, and impeding efforts to isolate positive cases. To address these shortcomings, this study develops a machine learning model based on the DenseNet-201 deep convolutional neural network, that can classify COVID-19 from chest radiographs in less than one minute and far more accurately than conventional tests (F1-score: 0.96; precision: 0.95; recall: 0.98). It uses a significantly larger dataset and more control classes than previously published models, demonstrating the promise of a machine learning approach for accurate and efficient COVID-19 screening. A live web application of the trained model can be accessed at https://cov2d19-classifier.herokuapp.com/.

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