“…This includes using pre-analysis methods to improve image quality by changing the contrast in the image and removal of unwanted background portions in the image. More robust feature extraction analysis methods will be investigated, including integrating the previous work related to using neural network [4] to analyse the LFA images to help to identify smaller changes in the Test line intensity.…”
Section: Future Workmentioning
confidence: 99%
“…This allows the reader to be more adaptable in analysing multiple types of LFAs for different medical conditions, including multiplex LFA strips with multiple biomarkers simultaneously. The analysis algorithm can also be constantly updated to improve the sensitivity of the analysis and also implement previous work with the CMOS camera images with the application of neural networks to analyse the LFA strips [4] . These analysis methods can be added to the images analysis algorithm presented in this paper, both onboard or on the cloud-based analysis platforms via software updates without requiring changes to the current hardware of the reader.…”
Objective: Respiratory disease is a rapidly growing global health issue that impacts the quality of living of tens of millions of people around the world. Neutrophil elastase (NE) represents a key inflammatory biomarker and has previously been demonstrated to have the capability of predicting exacerbation risk related to respiratory diseases. This paper utilises a low-cost Point of Care (PoC) approach using Lateral Flow Assays (LFAs) to provide quantitative measurement of active NE in a patient’s sputum. Methods and procedures: The main aim of this study is to develop a quantitative platform using a Complementary Metal-Oxide-Semiconductor (CMOS) to image the LFAs and with an adaptable image analysis algorithm to measure a target biomarker concentration. This result could be used to monitor a patient’s health and quality of living. In the paper, NE is used as the target biomarker to determine if the patient is suffering from a high risk of exacerbations. Results: The results presented in the paper indicate the CMOS reader approach is promising for rapid and low-cost PoC devices, with the current system able to provide quantitative trends of NE concentrations as low as 100 ng/ml and is comparable to a research-based laboratory lateral flow reader. Conclusion: The image analysis algorithm used in the CMOS reader can estimate the minimum NE concentration of 250 ng/ml to indicate the high-risk category for exacerbations from respiratory illnesses with the same accuracy as expensive a research-based laboratory reader but by using low-cost components and onboard image analysis. Clinical impact: The image analysis algorithm is evaluated to analyse LFAs with NE biomarker to determine the patient in a high-risk category for exacerbations. The device communicates the analysis result to medical professionals for daily historical logging for daily health monitoring without regular hospital appointments. The low-cost approach of the proposed system and image analysis approach can be adapted to analyse different biomarkers for other health concerns including multiplex LFAs without additional hardware in the reader design.
“…This includes using pre-analysis methods to improve image quality by changing the contrast in the image and removal of unwanted background portions in the image. More robust feature extraction analysis methods will be investigated, including integrating the previous work related to using neural network [4] to analyse the LFA images to help to identify smaller changes in the Test line intensity.…”
Section: Future Workmentioning
confidence: 99%
“…This allows the reader to be more adaptable in analysing multiple types of LFAs for different medical conditions, including multiplex LFA strips with multiple biomarkers simultaneously. The analysis algorithm can also be constantly updated to improve the sensitivity of the analysis and also implement previous work with the CMOS camera images with the application of neural networks to analyse the LFA strips [4] . These analysis methods can be added to the images analysis algorithm presented in this paper, both onboard or on the cloud-based analysis platforms via software updates without requiring changes to the current hardware of the reader.…”
Objective: Respiratory disease is a rapidly growing global health issue that impacts the quality of living of tens of millions of people around the world. Neutrophil elastase (NE) represents a key inflammatory biomarker and has previously been demonstrated to have the capability of predicting exacerbation risk related to respiratory diseases. This paper utilises a low-cost Point of Care (PoC) approach using Lateral Flow Assays (LFAs) to provide quantitative measurement of active NE in a patient’s sputum. Methods and procedures: The main aim of this study is to develop a quantitative platform using a Complementary Metal-Oxide-Semiconductor (CMOS) to image the LFAs and with an adaptable image analysis algorithm to measure a target biomarker concentration. This result could be used to monitor a patient’s health and quality of living. In the paper, NE is used as the target biomarker to determine if the patient is suffering from a high risk of exacerbations. Results: The results presented in the paper indicate the CMOS reader approach is promising for rapid and low-cost PoC devices, with the current system able to provide quantitative trends of NE concentrations as low as 100 ng/ml and is comparable to a research-based laboratory lateral flow reader. Conclusion: The image analysis algorithm used in the CMOS reader can estimate the minimum NE concentration of 250 ng/ml to indicate the high-risk category for exacerbations from respiratory illnesses with the same accuracy as expensive a research-based laboratory reader but by using low-cost components and onboard image analysis. Clinical impact: The image analysis algorithm is evaluated to analyse LFAs with NE biomarker to determine the patient in a high-risk category for exacerbations. The device communicates the analysis result to medical professionals for daily historical logging for daily health monitoring without regular hospital appointments. The low-cost approach of the proposed system and image analysis approach can be adapted to analyse different biomarkers for other health concerns including multiplex LFAs without additional hardware in the reader design.
“…Since the edge-filtered image can be produced by automatic edge detection tools, they can be used to train the model in a self-supervised way without needing a time-consuming process to collect manual labels. Note that compared to conventional approaches that use edge detectors (e.g., Sobel 58 , SIFT 39 , 59 , or canny edge detector 40 , 60 ) to directly extract features, our approach uses edge detection to generate the label for the self-supervision task to enforce the knowledge of edge sensitivity into the feature extractor. Fig.…”
Background
Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.
Methods
We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images.
Results
Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images.
Conclusions
The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.
“…28 Other models such as convolutional neural networks have been utilized for the detection of human chorionic gonadotropin, 29 malaria 30 and SARS-CoV-2 antibodies, 31 while recurrent neural network models have demonstrated high sensitivity in lateral flow immunoassays for C-reactive protein. 32 Despite these significant advancements, several challenges persist that restrict the broad-scale application of CV and ML in "sample-to-answer" POCT. Issues such as incorrect selection of a region of interest (ROI) can degrade the quality of digitized data, 27,33 while unregulated lighting conditions can adversely impact color intensity in colorimetric assays, frequently necessitating additional external hardware in smartphone-based assays.…”
Section: Introductionmentioning
confidence: 99%
“…28 Other models such as convolutional neural networks have been utilized for the detection of human chorionic gonadotropin, 29 malaria 30 and SARS-CoV-2 antibodies, 31 while recurrent neural network models have demonstrated high sensitivity in lateral flow immunoassays for C-reactive protein. 32…”
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