Hydrogel RT-LAMP: Tuberculosis Diagnostic Performance Metrics

A 2026 dataset by Jidong Wang on figshare presents performance metrics for a hydrogel-based diagnostic assay for tuberculosis. The data likely contains results from testing the HRT-LAMP method on 79 samples for threshold establishment and 91 samples for independent validation. It includes measures such as sensitivity, specificity, detection limits, and agreement with clinical diagnosis.

Use Cases

• Benchmarking novel diagnostic assays based on reported sensitivity (93.6%) and specificity (100%) metrics.
• Training or validating machine learning models for medical image analysis based on the convolutional neural network (CNN) mentioned for fluorescence quantification.
• Assessing field-deployable diagnostic platforms based on the described portable smartphone imaging and rapid (<4 h) test time.
• Evaluating biomarker performance for TB infection based on the CXCL10 mRNA target described in the study.

Strengths

• Validation on an independent cohort of 91 samples, achieving 91.2% agreement with clinical diagnosis.
• Demonstrated a wide dynamic range for detection, from 10 to 10^5 copies per test.
• Reported high sensitivity (92.3%) and specificity (89.7%) in the validation set, with an AUC of 0.918.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 25.4 KB, indicating limited scope.

Provenance

Source

Jidong Wang via figshare.

Collection Method

Data likely generated from experimental validation of a hydrogel RT-LAMP diagnostic assay.

Time Range

The dataset reflects research current as of its 2026 publication date.

Freshness

Last updated 2026-04-17 07:29:30; freshness should be verified.

Geography

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