A computer vision approach to the assessment of dried blood spot size and quality in newborn screening.

ABSTRACT

BACKGROUND: Dried blood spot (DBS) size and quality affect newborn screening (NBS) test results. Visual assessment of DBS quality is subjective. METHODS: We developed and validated a computer vision (CV) algorithm to measure DBS diameter and identify incorrectly applied blood in images from the Panthera DBS puncher. We used CV to assess historical trends in DBS quality and correlate DBS diameter to NBS analyte concentrations in 130,620 specimens. RESULTS: CV estimates of DBS diameter were precise (percentage coefficient of variation < 1.3%) and demonstrated excellent agreement with digital calipers with a mean (standard deviation) difference of 0.23 mm (0.18 mm). An optimised logistic regression model showed a sensitivity of 94.3% and specificity of 96.8% for detecting incorrectly applied blood. In a validation set of images (n = 40), CV agreed with an expert panel in all acceptable specimens and identified all specimens rejected by the expert panel due to incorrect blood application or DBS diameter > 14 mm. CV identified a reduction in unsuitable NBS specimens from 25.5% in 2015 to 2% in 2021. Each mm decrease in DBS diameter decreased analyte concentrations by up to 4.3%. CONCLUSIONS: CV can aid assessment of DBS size and quality to harmonize specimen rejection both within and between laboratories.