The Clinical Utility of Pollen Counts

Geller-Bernstein, C; Portnoy, Jay M.

doi:10.1007/s12016-018-8698-8

Cited by 34 publications

(25 citation statements)

References 57 publications

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“…Approximately 30% of the population of Germany is estimated to be sensitized to at least one type of aeroallergen [15]. To enable preventive measures or properly timed medication by pollen-allergic individuals it is useful to maintain an updated alert system [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Pollen information to warn of allergy risks is often provided by pollen stations located in cities, and in most cases only by one pollen station per city [19]. Traditional aerobiological sampling is labor-intensive and requires enormous amounts of human and economic resources [17]. Therefore, data from a single sampling point are extrapolated to a larger surrounding area.…”

Section: Introductionmentioning

confidence: 99%

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Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

et al. 2020

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Airborne pollen concentrations vary depending on the location of the pollen trap with respect to the pollen sources. Two Hirst-type pollen traps were analyzed within the city of Munich (Germany): one trap was located 2 m above ground level (AGL) and the other one at rooftop (35 m AGL), 4.2 km apart. In general, 1.4 ± 0.5 times higher pollen amounts were measured by the trap located at ground level, but this effect was less than expected considering the height difference between the traps. Pollen from woody trees such as Alnus, Betula, Corylus, Fraxinus, Picea, Pinus and Quercus showed a good agreement between the traps in terms of timing and intensity. Similar amounts of pollen were recorded in the two traps when pollen sources were more abundant outside of the city. In contrast, pollen concentrations from Cupressaceae/Taxaceae, Carpinus and Tilia were influenced by nearby pollen sources. The representativeness of both traps for herbaceous pollen depended on the dispersal capacity of the pollen grains, and in the case of Poaceae pollen, nearby pollen sources may influence the pollen content in the air. The timing of the pollen season was similar for both sites; however, the season for some pollen types ended later at ground level probably due to resuspension processes that would favor recirculation of pollen closer to ground level. We believe measurements from the higher station provides a picture of background pollen levels representative of a large area, to which local sources add additional and more variable pollen amounts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

et al. 2020

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“…Control measures and medication are readily available, but to alleviate the symptoms most efficiently, exposure to allergens should be kept to a minimum 3 . Therefore, for more and more people, fast and accurate monitoring of airborne pollen provides an essential early warning system 4 , 5 . Pollen concentrations in the air are monitored using samplers that collect airborne pollen on sticky tape, e.g.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for increased accuracy of allergenic pollen monitoring

Polling

Li²,

Cao³

et al. 2021

Sci Rep

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Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

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“…Control measures and medication are readily available, but to alleviate the symptoms most e ciently, exposure to allergens should be kept to a minimum 3 . Therefore, for more and more people, fast and accurate monitoring of airborne pollen provides an essential early warning system 4,5 . Pollen concentrations in the air are monitored using samplers that collect airborne pollen on sticky tape, e.g.…”

Section: Introductionmentioning

confidence: 99%

Automatic Image Classification Using Neural Networks Increases Accuracy for Allergenic Pollen Monitoring

Polling¹,

Li²,

Cao³

et al. 2020

Preprint

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Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen are traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of a Convolutional Neural Network (CNN) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species have very low allergenic relevance, those from several species of Parietaria are severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these to train the CNN model VGG16. The model shows that Urticaceae pollen can be distinguished with 98.3% accuracy. We then apply our model on Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

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The Clinical Utility of Pollen Counts

Cited by 34 publications

References 57 publications

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany

Neural networks for increased accuracy of allergenic pollen monitoring

Automatic Image Classification Using Neural Networks Increases Accuracy for Allergenic Pollen Monitoring

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