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REVIEW 4 major objections 5 minor 37 references

AI-Driven Smartphone Solution for Digitizing Rapid Diagnostic Test Kits and Enhancing Accessibility for the Visually Impaired

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that cropping the membrane region with YOLOv8 before CNN classification yields roughly 99.1% accuracy on rapid-test images, and that the crop is the cause of the improvement.

desk verdict A competent system paper whose central causal claim—YOLO cropping improves accuracy—rests on no comparison, plus several sloppy inconsistencies. read the letter →

arxiv 2411.18007 v1 pith:QAN4JA5J submitted 2024-11-27 cs.CV

R. B. Dastagir , J. T. Jami , S. Chanda , F. Hafiz , M. Rahman , K. Dey , M. M. Rahman , M. Qureshi
show 1 more author
M. M. Chowdhury
This is my paper · ORCID
classification cs.CV
keywords rapiddiagnostictestssmartphonediagnosticsYOLOv8convolutionalneuralnetworkspoint-of-careSHAPanalysislateralflowassaysvisualimpairmentaccessibility
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a smartphone-based system that reads rapid diagnostic tests (RDTs) automatically from a photo. A YOLOv8 detector first finds and crops the membrane region of the test kit, and a convolutional neural network then classifies the crop as positive, negative, or invalid and reports a confidence level. The authors report about 99.1% test accuracy and argue that the cropping step is what improves sensitivity and specificity, because it lets the classifier concentrate on the test and control lines rather than on background noise. They motivate the design as an accessibility aid: because the detector tolerates off-center or edge-of-frame test kits, a visually impaired user does not need perfect camera alignment. SHAP analysis is used to show that the network relies on the visible test and control lines and that the few errors come from external marks, smudges, or faded lines.

What carries the argument

The load-bearing mechanism is the two-stage extraction-then-classification pipeline. YOLOv8, a "You Only Look Once" object detector, is trained to place a bounding box around the membrane region, and that crop becomes the sole input to the CNN classifier. The paper's causal story is that cropping removes background clutter, brand text, and lighting artifacts, so the CNN can base its decision on the presence, absence, and intensity of the test and control lines. The second mechanism is SHAP (SHapley Additive exPlanations), which attributes each prediction to input pixels and is used to check that the model keys on the biological lines rather than on incidental image features.

What would settle it

Feed the same test-set images to the CNN once with the YOLO membrane crop and once with the full uncropped kit photo; if accuracy without cropping does not fall clearly below 99.1%, the central attribution of the gain to YOLO extraction is not supported. A second check would remove near-duplicate images between training and test sets and re-measure accuracy to see whether the reported figure reflects generalization or memorization.

Watch

Extended reading notes

Core claim

The central claim is that automatically extracting the membrane zone before classification materially improves rapid-test interpretation. The authors built a two-stage pipeline: YOLOv8 is trained on manually annotated kit images to detect the membrane, crop it, convert it to grayscale, and resize it; a five-convolutional-block CNN then classifies the crop. On a test set drawn from a previously published image collection plus images captured by the authors, the CNN reached roughly 99.1% accuracy, with per-class F1-scores of 0.985, 0.993, and 0.992 for invalid, negative, and positive, and macro precision and recall near 0.985 and 0.995. The authors attribute the high performance specifically to the YOLO membrane extraction, which they say separates genuine test lines from background noise. They further report that SHAP heatmaps place decision weight on the test and control lines, and that the four observed test-set misclassifications trace to external marks, smudges, or faint lines. A companion Android app uploads the photo to a server and returns a classification in roughly 11 seconds.

Load-bearing premise

The reported 99.1% test accuracy holds up on genuinely new smartphone photos, especially ones taken by visually impaired users, with no overlapping or repeated test-kit images across training and testing and no reliance on dataset-specific cues such as brand, background, or lighting.

Editorial extensions

If this is right

  • A user can photograph a rapid test without centering the kit and still receive an automated positive, negative, or invalid reading with a confidence score.
  • Interpreting faint or ambiguous test lines no longer depends solely on human visual judgment, so results should be more consistent across users.
  • The same crop-then-classify design can be retrained for other lateral-flow rapid tests, including veterinary and environmental assays, as the authors suggest.
  • Because the app returns a confidence level, a user or clinician can decide when to repeat or confirm a result.
  • SHAP heatmaps give developers a way to trace misclassifications to physical causes such as smudges or faded lines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • We infer that the claimed causal role of YOLO cropping has not actually been isolated because the paper reports no direct comparison between the CNN on cropped membranes and the same CNN on full uncropped kit photos, so the size of the cropping benefit remains untested.
  • We infer that the 99.1% figure is dataset-specific: the test set mixes a previously published image collection with the authors' own photos, and accuracy on unseen brands, lighting conditions, or user-captured images is not established.
  • We infer that the accessibility benefit for visually impaired users is a design inference rather than a measured outcome, because no user study with visually impaired participants is reported.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a smartphone-based pipeline for reading COVID-19 rapid diagnostic tests: a YOLOv8 model detects and crops the membrane region, and a CNN classifies the cropped image as positive, negative, or invalid. The authors report roughly 99.1% test accuracy on a mixed dataset of previously published and newly captured images, and use SHAP to qualitatively inspect model decisions. The main claimed contribution is that YOLO-based membrane extraction improves classification sensitivity and specificity, enabling use by visually impaired users who cannot center the test kit in the frame. The paper also describes a deployed Android app with a Flask backend that returns results via a cloud pipeline.

Significance. If the causal claim were established, this would be a useful practical contribution to point-of-care diagnostics and accessibility: an off-the-shelf pipeline that lets users photograph an RDT without careful alignment, then automatically crops and classifies the result. The reported accuracy is high, and the inclusion of a working mobile app and SHAP analysis are strengths. However, the central causal claim is not supported by the experiments as presented: there is no ablation or comparison to the same classifier on uncropped images, no analysis of how YOLO cropping errors affect final classification, and no error bars or external validation. The paper's significance therefore hinges on an untested assumption, and the current evaluation is insufficient to justify the title's accessibility claim beyond a proof-of-concept.

major comments (4)
  1. [Abstract and §3.2] The abstract states that improved sensitivity and specificity 'can be attributed to the extraction of the membrane zones' using YOLO, but §3.2 only reports the end-to-end confusion matrix on cropped images. There is no comparison with the same CNN trained or tested on full, uncropped smartphone images, no ablation of the YOLO step, and no analysis of the effect of YOLO cropping errors on test predictions. As written, the 99.1% accuracy could be due to the classifier alone or to dataset-specific cues, not to the cropping mechanism. Please add a controlled experiment, e.g., the same CNN on full images versus cropped images, and report the difference.
  2. [§2.3] The data split is described as '80 batches' with 54/12/12 for train/validation/test, but it is not stated whether a 'batch' corresponds to a physical test kit, a capture session, or individual images. If multiple near-duplicate images from the same kit or the same photo session appear in both training and test partitions, the reported accuracy would be inflated by memorization rather than generalization. Please clarify the grouping unit and, ideally, split by physical test kit or capture session to ensure test-set independence.
  3. [§3.2 and Figure 11] The confusion matrix and the 'approximately 99.1%' accuracy are presented without confidence intervals or any measure of uncertainty. With 456 test images and only 4 misclassifications, the binomial 95% confidence interval spans roughly 97.5% to 99.8%, so the reported figure is not distinguishable from other high-performing classifiers. Please provide confidence intervals and, if possible, bootstrap or repeated-seed results. A comparison with human readers or with previously published automated readers (e.g., the ALFA system or Mendels et al. [24]) would also help calibrate the practical significance.
  4. [§3.3] The SHAP analysis is used to infer that the model relies on test/control lines and that misclassifications are due to smudges or faint lines, but the evidence is qualitative: a handful of heatmaps and the four incorrect test-set examples. It does not quantify the contribution of the YOLO cropping step, and the attribution of misclassifications to specific image marks is post-hoc and not statistically tested. The section also confusingly refers to 'pregnancy test' results in two places, which is inconsistent with the COVID-19 RDT context. Please correct the text and, if the SHAP analysis is intended to support the cropping claim, add a more systematic evaluation, e.g., comparing SHAP maps on correctly and incorrectly cropped images.
minor comments (5)
  1. [§2.3 and Table 2/Figure 5] There is an inconsistency in input size: §2.3 states images are resized to 256×256 pixels, while Table 2 and Figure 5 state 128×128. Please reconcile these numbers.
  2. [References] Several in-text citations appear to be misattributed. For example, the introduction credits 'Krizhevsky et al. [20]' with developing the xRCovid app, but xRCovid is described in the literature as the work of Dortet et al. [13]; reference [4] (van Grinsven et al.) is cited as an LFA study but the actual title is about hemorrhage detection in fundus images; and reference [7] and [24] both involve Mendels et al. but describe different works. Please carefully verify each citation against the reference list and correct the attributions.
  3. [§3.3] The text in the SHAP section refers to 'classifying pregnancy test results' and 'pregnancy test membrane' when the paper is about COVID-19 RDTs. Please replace these with the correct terminology.
  4. [§3.3, Figures 12–13] The sentence 'In Figure 12(a), the sample is incorrectly classified as Positive instead of Negative' refers to misclassified samples, but Figure 12 is captioned as 'correctly classified samples.' The incorrect samples are shown in Figure 13. Please correct the figure reference.
  5. [§2.1] Ground-truth labeling is described as 'reviewed at least twice by human analysts, with a third review if needed,' but no inter-reviewer agreement or quality-control metric is reported. A brief note on labeling reliability would strengthen the dataset description.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 99.1% accuracy is an end-to-end held-out test result; the unsupported causal claim about YOLO cropping is an evidence gap, not a definitional circularity.

full rationale

The paper's central quantitative claim is a held-out test accuracy of approximately 99.1%, reported in Section 3.2 from a confusion matrix on test batches reserved in Section 2.3. That is an end-to-end evaluation, not a fitted parameter renamed as a prediction. The YOLOv8 membrane extraction is trained on manually annotated bounding boxes (Section 2.2) and is a fixed preprocessing stage for the separately trained CNN; the accuracy result does not reduce by construction to the training inputs. The causal statement in the abstract that the improvement 'can be attributed to the extraction of the membrane zones' is unsupported because no ablation against uncropped images is reported, but an unsupported causal attribution is a completeness or evidence limitation, not circularity. The external dataset from Mendels et al. [24] is prior work by non-overlapping authors and is used as independent data, not as a self-citation carrying the argument. SHAP analysis in Section 2.4 explains the trained model's decisions rather than generating the accuracy result. No uniqueness theorem, ansatz, or renamed known result is used as a load-bearing circular step. The main weakness is the missing counterfactual for the claimed benefit of YOLO cropping, which affects confidence in the mechanism but does not make the derivation circular.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central empirical evaluation rests on the assumption that the labels are correct and that the test split is independent and representative. No new physical or conceptual entities are introduced.

free parameters (5)
  • CNN input image resolution = 128x128 (Table 2) vs 256x256 (Section 2.3 text)
    The paper specifies two different input sizes for the classifier; this is a hand-chosen design parameter that affects the model.
  • Dropout rate = 0.2
    Applied to all dropout layers without reporting any tuning or ablation.
  • CNN filter counts and dense units = 128, 128, 64, 64, 32 filters; 16 dense units
    Hand-designed architecture; no justification or ablation shown.
  • Train/validation/test split ratios = 80/20 for YOLO; 70/15/15 for CNN
    The split ratios are chosen without explanation; the exact number of images in each split is not fully specified.
  • YOLO training hyperparameters = not reported
    Epochs, batch size, learning rate, and augmentation settings are not stated, preventing replication.
assumptions (4)
  • domain assumption Manual labels assigned by human analysts are correct ground truth.
    The paper states each image was reviewed at least twice by human analysts, but no reference assay or independent verification is used to confirm the labels.
  • domain assumption Test images are independent of training images.
    The dataset combines a previously published set with new captures; no deduplication or overlap analysis is described.
  • domain assumption Softmax probabilities can be interpreted as confidence.
    The app displays the CNN softmax output as a confidence level, but neural network softmax probabilities are not necessarily calibrated.
  • domain assumption The membrane region cropped by YOLO contains all information needed for classification.
    The classifier only sees the cropped bounding box; any cues outside the box are discarded.

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Cite this review

Pith. "Pith review of AI-Driven Smartphone Solution for Digitizing Rapid Diagnostic Test Kits and Enhancing Accessibility for the Visually Impaired." pith.science (2026). https://pith.science/paper/QAN4JA5J

@misc{pith2026241118007,
  author       = {Pith},
  title        = {Pith review of: AI-Driven Smartphone Solution for Digitizing Rapid Diagnostic Test Kits and Enhancing Accessibility for the Visually Impaired},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QAN4JA5J}},
  note         = {Machine review of arXiv:2411.18007}
}
read the original abstract

Rapid diagnostic tests are crucial for timely disease detection and management, yet accurate interpretation of test results remains challenging. In this study, we propose a novel approach to enhance the accuracy and reliability of rapid diagnostic test result interpretation by integrating artificial intelligence (AI) algorithms, including convolutional neural networks (CNN), within a smartphone-based application. The app enables users to take pictures of their test kits, which YOLOv8 then processes to precisely crop and extract the membrane region, even if the test kit is not centered in the frame or is positioned at the very edge of the image. This capability offers greater accessibility, allowing even visually impaired individuals to capture test images without needing perfect alignment, thus promoting user independence and inclusivity. The extracted image is analyzed by an additional CNN classifier that determines if the results are positive, negative, or invalid, providing users with the results and a confidence level. Through validation experiments with commonly used rapid test kits across various diagnostic applications, our results demonstrate that the synergistic integration of AI significantly improves sensitivity and specificity in test result interpretation. This improvement can be attributed to the extraction of the membrane zones from the test kit images using the state-of-the-art YOLO algorithm. Additionally, we performed SHapley Additive exPlanations (SHAP) analysis to investigate the factors influencing the model's decisions, identifying reasons behind both correct and incorrect classifications. By facilitating the differentiation of genuine test lines from background noise and providing valuable insights into test line intensity and uniformity, our approach offers a robust solution to challenges in rapid test interpretation.

Figures

Figures reproduced from arXiv: 2411.18007 by the authors.

Figure 1
Figure 1. Mendels et al. [24] prepared an image dataset of rapid test kits with positive and negative results confirmed by two human analysts. The images were taken using a camera positioned at an elevation of 98 mm from the reference plane. Selective images from this dataset were used in the current work to train and validate the performance of our model. Images of rapid test kits from multiple brands with “POSITIVE” and “NE… view at source ↗
Figure 3
Figure 3. Images of the membranes of the test kit are extracted using the YOLO v8 algorithm, the cropped images are then converted into grayscale and subsequently rescaled to 256 × 256 pixel. Image of rapid test kit collected from Mendels et al. [24] [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Neural network architecture of the Deep Learning Model that has been used in this study where the sequential layer is followed by multiple convolutional layers, max pooling layers, and dropout layers. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: The process diagram of our proposed model is shown. First, the raw images are cropped using a custom-trained YOLO v8 model to extract the membrane zones. The cropped images are then converted to grayscale and resized to 128x128 pixels for model training. The model's pe…
Figure 6
Figure 6. Figure 6: Schematic of the working principle of the mobile app developed for automated rapid test kit detection using an Android phone’s camera. After an image is taken, the image blob is sent to the [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 9
Figure 9. Figure 9: Improvement in (a) accuracy and (b) loss with an increasing number of epochs while training the convolutional neural network model [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 12
Figure 12. Figure 12: SHAP Analysis of three sample correctly classified samples from the test dataset. a) A Positive Sample Predicted as Positive b) An Invalid sample predicted as Invalid c) A Negative sample predicted as Negative. The consistent presence of red highlights across differen…
Figure 13
Figure 13. Figure 13: SHAP Analysis of all four incorrect results from the predictions on test dataset a) A Negative sample classified as Positive b) A Negative Sample classified as Invalid c) A Positive sample classified as Negative d) A Positive sample classified as Negative. In [PITH_F…

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Works this paper leans on

37 extracted references · 37 canonical work pages

  1. [24]

    Using artificial intelligence to improve COVID -19 rapid diagnostic test result interpretation,

    D. A. Mendels, L. Dortet, C. Emeraud, S. Oueslati, D. Girlich, J. B. Ronat, S. Bernabeu, S. Bahi, G. J. H. Atkinson, and T. Naas, "Using artificial intelligence to improve COVID -19 rapid diagnostic test result interpretation," Proc. Natl. Acad. Sci. U. S. A., vol. 118, no. 12, pp. e2019893118, Mar. 2021

    work page 2021

  2. [1]

    Lateral flow (immuno) assay: its strengths, weaknesses, opportunities, and threats,

    G. A. Posthuma -Trumpie, J. Korf, and A. L. van Amerongen, "Lateral flow (immuno) assay: its strengths, weaknesses, opportunities, and threats," Analytical and Bioanalytical Chemistry, vol. 393, pp. 569-582, 2009

    work page 2009

  3. [2]

    Lateral flow assays,

    K. M. Koczula and A. L. Gallotta, "Lateral flow assays," Essays in Biochemistry, vol. 60, pp. 111 - 120, 2016

    work page 2016

  4. [3]

    On the challenges for the diagnosis of SARS-CoV-2 based on a review of current methodologies,

    I. A. Mattioli, A. Hassan, O. N. Oliveira Jr., and F. N. Crespilho, "On the challenges for the diagnosis of SARS-CoV-2 based on a review of current methodologies," ACS Sensors, vol. 5, pp. 3655 -3677, 2020

    work page 2020

  5. [4]

    Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images,

    M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, "Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images," IEEE Transactions on Medical I maging, vol. 35, pp. 1273 -1284, 2016

    work page 2016

  6. [5]

    Significant sensitivity improvement for camera-based lateral flow immunoassay readers,

    L. Saisin, R. Amarit, A. Somboonkaew, O. Gajanandana, O. Himananto, and B. Sutapun, "Significant sensitivity improvement for camera-based lateral flow immunoassay readers," Sensors, vol. 18, no. 11, p. 4026, Nov. 2018

    work page 2018

  7. [6]

    Antibody responses to SARS -CoV-2 in patients with COVID-19,

    Q. X. Long, B. Z. Liu, H. J. Deng, G. C. Wu, K. Deng, Y. K. Chen, P. Liao, J. F. Qiu, Y. Lin, X. F. Cai, D. Q. Wang, Y. Hu, J. H. Ren, N. Tang, Y. Y. Xu, L. H. Yu, Z. Mo, F. Gong, X. L. Zhang, W. G. Tian, L. Hu, X. X. Zhang, J. L. Xiang, H. X. Du, H. W. L iu, C. H. Lang, X. H. Luo, S. B. Wu, X. P. Cui, Z. Zhou, M. M. Zhu, J. Wang, C. J. Xue, X. F. Li, L. ...

    work page 2020

  8. [7]

    Artificial intelligence -enhanced lateral flow immunoassay for tuberculosis,

    D. A. Mendels, M. L. Schapira, A. Murison, et al., "Artificial intelligence -enhanced lateral flow immunoassay for tuberculosis," Science Advances, vol. 7, no. 25, p. eabg5185, 2021

    work page 2021

Show all 37 references
  1. [8]

    Performance of the Innova SARS -CoV-2 Antigen Rapid Lateral Flow Test in the Liverpool Asymptomatic Testing Pilot,

    M. García -Fiñana, D. M. Hughes, C. P. Cheyne, et al., "Performance of the Innova SARS -CoV-2 Antigen Rapid Lateral Flow Test in the Liverpool Asymptomatic Testing Pilot," SSRN, 2021

    2021

  2. [9]

    COVID -19: rapid antigen detection for SARS -CoV-2 by lateral flow assay: a national systematic evaluation for mass-testing,

    T. Peto, "COVID -19: rapid antigen detection for SARS -CoV-2 by lateral flow assay: a national systematic evaluation for mass-testing," medRxiv, 2021

    2021

  3. [10]

    Wang et al., The application of lateral flow immunoassay in point of care testing: A review

    K. Wang et al., The application of lateral flow immunoassay in point of care testing: A review. Nano Biomed. Eng. 8, 172–183 (2016)

    2016

  4. [11]

    SARS-CoV-2 diagnostic pipeline,

    Foundation for Innovative New Diagnostics, "SARS-CoV-2 diagnostic pipeline," Foundation for Innovative New Diagnostics, Geneva, Switzerland, Jun. 15, 2020

    2020

  5. [12]

    Diagnostic indexes of a rapid IgG/IgM combined antibody test for SARS-CoV-2,

    Y. Liu, Y. Liu, B. Diao, F. Ren, Y. Wang, J. Ding, Q. Huang, et al., "Diagnostic indexes of a rapid IgG/IgM combined antibody test for SARS-CoV-2," medRxiv, Mar. 2, 2021

    2021

  6. [13]

    Rapid determination of SARS-CoV-2 antibodies using a bedside, point- of-care, serological test,

    L. Dortet, C. Emeraud, C. Vauloup -Fellous, M. Khecharem, J. -B. Ronat, N. Fortineau, A. -M. Roque-Afonso, and T. Naas, "Rapid determination of SARS-CoV-2 antibodies using a bedside, point- of-care, serological test," SSRN, Apr. 20, 2020

    2020

  7. [14]

    Machine -Learning-Assisted Lateral Flow Assay for COVID -19 and Influenza Detection,

    S. Lee, Y. K. Yoo, K. W. Wee, C. Kim, N. E. Lee, K. H. Kim, H. Kim, D. Lee, S. I. Han, D. Lee, and D. S. Yoon, "Machine -Learning-Assisted Lateral Flow Assay for COVID -19 and Influenza Detection," SSRN, 2022

    2022

  8. [15]

    Immunological assays for SARS -CoV-2: an analysis of available commercial tests to measure antigen and antibodies,

    J. Gonzalez, W. Shelton, D. -V. Manuel, V. E. Rodriguez -Castellanos, J. D. H. Zuluaga, D. F. Chamorro, and D. Arroyo -Ariza, "Immunological assays for SARS -CoV-2: an analysis of available commercial tests to measure antigen and antibodies," medRxiv, 2020

    2020

  9. [16]

    GelApp: Mobile gel electrophoresis analyser,

    J. -Z. Sim, P. -V. Nguyen, and S. K. -E. Gan, "GelApp: Mobile gel electrophoresis analyser," Scientific Phone Apps and Mobile Devices, vol. 5, no. 4, 2015

    2015

  10. [17]

    The world of biomedical apps: their uses, limitations, and potential,

    S. K.-E. Gan and J.-K. Poon, "The world of biomedical apps: their uses, limitations, and potential," Scientific Phone Apps and Mobile Devices, vol. 2, no. 6, 2016

    2016

  11. [18]

    Fast detection of SARS-CoV-2 RNA via the integration of plasmonic thermocycling and fluorescence detection in a portable device,

    J. Cheong, H. Yu, C. Y. Lee, J.-u. Lee, H.-J. Choi, J.-H. Lee, H. Lee, and J. Cheon, "Fast detection of SARS-CoV-2 RNA via the integration of plasmonic thermocycling and fluorescence detection in a portable device," Nature Biomedical Engineering, vol. 4, no. 12, pp. 1159-1167, 2020

    2020

  12. [19]

    Editorial: The promises of Microcontroller kits and Smartphone apps for psychological research,

    S. K.-E. Gan and J. Y. Yeo, "Editorial: The promises of Microcontroller kits and Smartphone apps for psychological research," Scientific Phone Apps and Mobile Devices, vol. 6, no. 2, 2020

    2020

  13. [20]

    Krizhevsky, I

    A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM, 2017

    2017

  14. [21]

    Point-of-care serodiagnostic test for early-stage lyme disease using a multiplexed paper-based immunoassay and machin e learning,

    H.-A. Joung, Z. S. Ballard, J. Wu, D. K. Tseng, H. Teshome, L. Zhang, E. J. Horn, P. M. Arnaboldi, R. J. Dattwyler, and O. B. Garner, "Point-of-care serodiagnostic test for early-stage lyme disease using a multiplexed paper-based immunoassay and machin e learning," ACS Nano, v...

    2019

  15. [22]

    Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network,

    N. Zeng, H. Li, Y. Li and X. Luo, "Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network," in IEEE Access, vol. 7, pp. 16257-16263, 2019

    2019

  16. [23]

    Digital technologies in the public -health response to COVID-19,

    J. Budd, B. S. Miller, E. M. Manning, et al., "Digital technologies in the public -health response to COVID-19," Nat. Med., vol. 26, pp. 1183–1192, 2020

    2020

  17. [25]

    User experience analysis of AbC-19 Rapid Test via lateral flow immunoassays for self-administrated SARS-CoV-2 antibody testing,

    M. Jing, R. Bond, L. J. Robertson, et al., "User experience analysis of AbC-19 Rapid Test via lateral flow immunoassays for self-administrated SARS-CoV-2 antibody testing," Sci. Rep., vol. 11, p. 14026, 2021

    2021

  18. [26]

    Prevalence of antibody positivity to SARS -CoV-2 following the first peak of infection in England: Serial cross - sectional studies of 365,000 adults,

    H. Ward, G. S. Cooke, C. Atchison, M. Whitaker, J. Elliott, M. Moshe, J. C. Brown, B. Flower, A. Daunt, K. Ainslie, D. Ashby, C. A. Donnelly, S. Riley, A. Darzi, W. Barclay, and P. Elliott, "Prevalence of antibody positivity to SARS -CoV-2 following the first peak of infection...

    2021

  19. [27]

    TianhongDai/react2-code: REACT2 v2.0.0

    Dai, T. TianhongDai/react2-code: REACT2 v2.0.0. Zenodo

  20. [28]

    Home testing past, present and future: lessons learned and implications for HIV home tests,

    M. Ibitoye, T. Frasca, R. Giguere, and A. Carballo-Diéguez, "Home testing past, present and future: lessons learned and implications for HIV home tests," AIDS and Behavior, vol. 18, pp. 933-949, 2014

    2014

  21. [29]

    Automated low-cost smartphone-based lateral flow saliva test reader for drugs -of-abuse detection,

    A. Carrio, C. Sampedro, J. L. Sanchez-Lopez, M. Pimienta, and P. Campoy, "Automated low-cost smartphone-based lateral flow saliva test reader for drugs -of-abuse detection," Sensors, vol. 15, pp. 29569-29593, 2015

    2015

  22. [30]

    Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR,

    V. M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D. K. W. Chu, T. Bleicker, S. Brunink, J. Schneider, M. L. Schmidt, D. G. J. C. Mulders, B. L. Haagmans, B. van der Veer, S. van den Brink, L. Wijsman, G. Goderski, J. -L. Romette, J. Ellis, M. Zambon, M. Peiris, H. G...

    2019

  23. [31]

    Six decades of lateral flow immunoassay: from determining metabolic markers to diagnosing COVID-19,

    B. G. Andryukov, "Six decades of lateral flow immunoassay: from determining metabolic markers to diagnosing COVID-19," AIMS Microbiology, vol. 6, pp. 280-304, 2020

    2020

  24. [32]

    Usability and acceptability of home-based self-testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies for population surveillance,

    C. Atchison, P. Pristerà, E. Cooper, V. Papageorgiou, R. Redd, M. Piggin, B. Flower, G. Fontana, S. Satkunarajah, H. Ashrafian, A. Lawrence -Jones, L. Naar, J. Chigwende, S. Gibbard, S. Riley, A. Darzi, P. Elliott, D. Ashby, W. Barclay, G. S. Cooke, and H. Ward, "Usability and...

    2021

  25. [33]

    Clinical and laboratory eval uation of SARS -CoV-2 lateral flow assays for use in a national COVID -19 seroprevalence survey,

    B. Flower, J. C. Brown, B. Simmons, M. Moshe, R. Frise, R. Penn, R. Kugathasan, C. Petersen, A. Daunt, D. Ashby, S. Riley, C. J. Atchison, G. P. Taylor, S. Satkunarajah, L. Naar, R. Klaber, A. Badhan, C. Rosadas, M. Khan, N. Fernandez, M. Sureda-Vives, H. M. Cheeseman, J. O'Ha...

    2020

  26. [34]

    Roboflow (Version 1.0) [Software],

    B. Dwyer, J. Nelson, T. Hansen, et al., "Roboflow (Version 1.0) [Software]," 2024

    2024

  27. [35]

    Jocher, A

    G. Jocher, A. Chaurasia, and J. Qiu, Ultralytics YOLO (Version 8.0.0) [Software]. Ultralytics, Jan. 10, 2023

    2023

  28. [36]

    A Unified Approach to Interpreting Model Predictions,

    S. M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in Advances in Neural Information Processing Systems, vol. 30, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., Curran Associates, Inc., 2017

    2017

  29. [37]

    Flask (Version 2.2.5) [Software],

    M. Grinberg, A. Ronacher, D. Uretsky, et al., "Flask (Version 2.2.5) [Software]," 2024

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.