Dataset Information

A rapid and affordable point of care test for antibodies against SARS-CoV-2 based on hemagglutination and artificial intelligence interpretation.

ABSTRACT: Diagnostic tests that detect antibodies (AB) against SARS-CoV-2 for evaluation of seroprevalence and guidance of health care measures are important tools for managing the COVID-19 pandemic. Current tests have certain limitations with regard to turnaround time, costs and availability, particularly in point-of-care (POC) settings. We established a hemagglutination-based AB test that is based on bi-specific proteins which contain a dromedary-derived antibody (nanobody) binding red blood cells (RBD) and a SARS-CoV-2-derived antigen, such as the receptor-binding domain of the Spike protein (Spike-RBD). While the nanobody mediates swift binding to RBC, the antigen moiety directs instantaneous, visually apparent hemagglutination in the presence of SARS-CoV-2-specific AB generated in COVID-19 patients or vaccinated individuals. Method comparison studies with assays cleared by emergency use authorization demonstrate high specificity and sensitivity. To further increase objectivity of test interpretation, we developed an image analysis tool based on digital image acquisition (via a cell phone) and a machine learning algorithm based on defined sample-training and -validation datasets. Preliminary data, including a small clinical study, provides proof of principle for test performance in a POC setting. Together, the data support the interpretation that this AB test format, which we refer to as 'NanoSpot.ai', is suitable for POC testing, can be manufactured at very low costs and, based on its generic mode of action, can likely be adapted to a variety of other pathogens.

SUBMITTER: Redecke V

PROVIDER: S-EPMC8718524 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A rapid and affordable point of care test for antibodies against SARS-CoV-2 based on hemagglutination and artificial intelligence interpretation.

Redecke Vanessa V Tawaratsumida Kazuki K Larragoite Erin T ET Williams Elizabeth S C P ESCP Planelles Vicente V Spivak Adam M AM Hirayama Lincoln L Elgort Marc M Swenson Shane S Smith Rick R Worthen Bryan B Zimmerman Russ R Slev Patricia P Cahoon Ben B Astill Mark M Häcker Hans H

Scientific reports 20211230 1

Diagnostic tests that detect antibodies (AB) against SARS-CoV-2 for evaluation of seroprevalence and guidance of health care measures are important tools for managing the COVID-19 pandemic. Current tests have certain limitations with regard to turnaround time, costs and availability, particularly in point-of-care (POC) settings. We established a hemagglutination-based AB test that is based on bi-specific proteins which contain a dromedary-derived antibody (nanobody) binding red blood cells (RBD) ...[more]

PMID: 34969960

Similar Datasets

Project description:ImportanceElectroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed.ObjectiveTo develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse.Design, setting, and participantsIn this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded.Main outcomes and measuresDiagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording.ResultsThe characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts.Conclusions and relevanceIn this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

Project description:Access to reverse transcription-PCR (RT-PCR) testing, the gold standard for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection, is limited throughout the world, due to restricted resources, available infrastructure, and high costs. Antigen-detecting rapid diagnostic tests (Ag-RDTs) overcome some of these barriers, but independent clinical validations in settings of intended use are scarce. To inform the World Health Organization's (WHO) emergency use listing (EUL) procedure and ensure affordable, high-quality Ag-RDTs, we assessed the performance and ease of use of the SureStatus for SARS-CoV-2. For this prospective, multicenter diagnostic accuracy study, we recruited unvaccinated participants with presumed SARS-CoV-2 infection in India and Germany from December 2020 to March 2021, when the Alpha (B.1.1.7) variant was predominantly circulating. Paired swabs were performed for (i) routine clinical RT-PCR testing (sampling was either nasopharyngeal [NP] or combined NP and oropharyngeal [NP/OP]) and (ii) Ag-RDT (sampling was NP). Performance of the Ag-RDT was compared to RT-PCR overall and by predefined subgroups, e.g., cycle threshold (CT) value, symptoms, and days from symptom onset. To understand the usability, a system usability scale (SUS) questionnaire and ease-of-use (EoU) assessment were performed. A total of 1,119 participants were included in the analysis, of whom 205 (18.3%) were RT-PCR positive. SureStatus detected 169 out of 205 RT-PCR-positive participants, reporting a sensitivity of 82.4% (95% confidence interval [CI]: 76.6% to 87.1%) and a specificity of 98.5% (95% CI: 97.4% to 99.1%). In the first 7 days post-symptom onset, the sensitivity was 90.7% (95% CI: 83.5% to 94.9%), when CT values were low and viral loads were high. The test was characterized as easy to use (SUS, 85/100) and considered suitable for point-of-care settings, although quality concerns were raised due to visibly contaminated packaging of swabs included in the test kits. The SureStatus diagnostic test can be considered a reliable test during the first week of SARS-CoV-2 infection, with high sensitivity in combination with excellent usability. IMPORTANCE Our manufacturer-independent, prospective diagnostic accuracy study assessed clinical performance in participants presumed to have a SARS-CoV-2 infection at three study sites in two countries. We assessed the accuracy overall and in predefined subgroups (CT values and symptom duration). SureStatus performed with high sensitivity. Its sensitivity was particularly high in the first 3 days after symptom onset and when CT values were low (i.e., the viral load was high). The system usability and ease-of-use assessment complements the accuracy assessment of the test and highlights critical factors to facilitate the widespread use of SureStatus in point-of-care settings. The high sensitivity demonstrated by the evaluated Ag-RDT within the first days of symptoms, when most transmission occurs, supports the role of Ag-RDTs for public health-relevant screening. Evidence from this study was used to inform the World Health Organization Emergency Use Listing procedure.

Project description:To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice. This was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality. Overall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]). Instead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists. • Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%). • The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality. • Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis.

Dataset Information

A rapid and affordable point of care test for antibodies against SARS-CoV-2 based on hemagglutination and artificial intelligence interpretation.

Publications

A rapid and affordable point of care test for antibodies against SARS-CoV-2 based on hemagglutination and artificial intelligence interpretation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets