A computer vision approach to the assessment of dried blood spot size and quality in newborn screening.
Artificial intelligence
Computer vision
Dried blood spot quality
Machine learning
Newborn screening
Journal
Clinica chimica acta; international journal of clinical chemistry
ISSN: 1873-3492
Titre abrégé: Clin Chim Acta
Pays: Netherlands
ID NLM: 1302422
Informations de publication
Date de publication:
01 Jul 2023
01 Jul 2023
Historique:
received:
03
04
2023
revised:
01
06
2023
accepted:
01
06
2023
medline:
10
7
2023
pubmed:
6
6
2023
entrez:
5
6
2023
Statut:
ppublish
Résumé
Dried blood spot (DBS) size and quality affect newborn screening (NBS) test results. Visual assessment of DBS quality is subjective. 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. 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%. CV can aid assessment of DBS size and quality to harmonize specimen rejection both within and between laboratories.
Sections du résumé
BACKGROUND
BACKGROUND
Dried blood spot (DBS) size and quality affect newborn screening (NBS) test results. Visual assessment of DBS quality is subjective.
METHODS
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
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
CONCLUSIONS
CV can aid assessment of DBS size and quality to harmonize specimen rejection both within and between laboratories.
Identifiants
pubmed: 37276944
pii: S0009-8981(23)00220-6
doi: 10.1016/j.cca.2023.117418
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
117418Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.