Automated Classification of At-home SARS-CoV-2 Lateral Flow Assay Test Results using Image Matching and Transfer Learning: multiple-pipeline study

Introduction Rapid antigen testing for SARS-CoV-2 is an important tool for the timely diagnosis of COVID-19, especially in at-home settings. However, the interpretation of test results can be subjective and prone to error. We describe an automated image analysis pipeline to accurately classify test types and results without human intervention using a dataset of 51,274 rapid antigen test images across three distinct test card brands. Methods The proposed method classifies participant-submitted images into four categories: positive for SARS-CoV-2, negative for SARS-CoV-2, invalid/uncertain, and unclassifiable. The model includes four stages: test card classification and region of interest detection using image-matching algorithms, elimination of invalid results using a developed Siamese neural network, and test result classification using transfer learning. Results The model accuracy was very good for test-card classification (100%), region of interest detection (83.5%), and identification of invalid results ranging from 95.6% to 100% for different test types. Performance of the model for test result classification varied by tests; the model’s sensitivity, specificity, and precision for Abbott BinaxNOW™ was 0.761, 0.989, and 0.946, BD Veritor™ At-Home COVID-19 Test was 0.955, 0.993, and 0.877, and for QuickVue® At-Home OTC COVID-19 Test was 0.816, 0.988, and 0.930. Conclusion The proposed method improved the interpretation of rapid antigen tests, particularly in invalid result detection compared to human-read, and offers a great opportunity for standardization of rapid antigen test interpretation and for providing feedback to participants with invalid tests. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Dr. McManus is supported by NHLBI grants R01HL155343, R01HL141434, R33HL158541, and U54HL143541. Dr. McManus reports receiving research support (either grants or material) from Apple Computer, Bristol-Myers Squibb, Boehringer-Ingelheim, Pfizer, Samsung, Flexcon, Philips Healthcare, and Biotronik; consultancy fees from Bristol-Myers Squibb, Pfizer, Flexcon, Avania, NAMSA, Fitbit, and Heart Rhythm Society (for editorial work). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The ethics IRB of the University of Massachusetts Chan Medical School gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors