Prevention, Early Dialogue and Education in the Personalised Healthcare Era

Horgan, Denis; Pazzagli, Mario

doi:10.1159/000479492

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Pollen of many wind-pollinated plants has specific proteins that cause human allergies (Valenta et al, 1992;Bousquet et al, 2006Bousquet et al, , 2015Radauer and Breiteneder, 2006;Choual et al, 2018), particularly affecting children (Skoner, 2001;Høst et al, 2003;Douladiris et al, 2018). Stress due to contact with the pollen-contained allergen can cause an allergic reaction or exacerbate some related diseases (Leynaert et al, 2000;Devillier et al, 2017;Poethko-Müller et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

Šaulienė¹,

Šukienė²,

Daunys³

et al. 2019

Atmos. Meas. Tech.

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Abstract. Pollen-induced allergies are among the most prevalent non-contagious diseases, with about a quarter of the European population being sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive and requires narrow specialized abilities and substantial time, so that the pollen data are always delayed and subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct a first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANNs) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence-based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better, exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species, ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus) and (Festuca, Artemisia and Juniperus). Within these groups, pollen is practically indistinguishable for the first-level recognition procedure. Construction of multistep algorithms with sequential discrimination of pollen inside each group seems to be one of the possible ways forward. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and the issue of false positive pollen detections by Rapid-E is discussed.

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Section: Introductionmentioning

confidence: 99%

Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

Šaulienė¹,

Šukienė²,

Daunys³

et al. 2019

Atmos. Meas. Tech.

View full text Add to dashboard Cite

show abstract

“…However, timely data about pollen concentration in the air are also needed for improving the accuracy of tools for personalized medicine (for example, PASYFO app, http://www.pasyfo.lt, POLLEN app, http://www.polleninfo.org, NORKKO forecast and app http://www.norkko.fi, etc.) (Bousquet et al, 2017;Horgan and Pazzagli, 2017;Pereira et al, 2018, Tabatabaian andCasale 2018). It can be also used for informing people about current pollen concentration in the air.…”

Section: Introductionmentioning

confidence: 99%

Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

Šaulienė¹,

Šukienė²,

Daunys³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Pollen-induced allergy is among the most-prevalent non-contagious diseases, with about a quarter of European population sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive, requires narrow specialization abilities and substantial time, so that the pollen data are always delayed, subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct the first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANN) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence- based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus), and (Festuca, Artemisia, Juniperus). Within these groups, pollen is practically non-distinguishable for the first-level recognition procedure. Construction of multi-steps algorithms with sequential discrimination of pollen inside each group seems to be one of possible ways forwards. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and an issue of the false-positive pollen detections by Rapid-E is discussed.

show abstract

Prevention, Early Dialogue and Education in the Personalised Healthcare Era

Cited by 2 publications

References 17 publications

Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps

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