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-
1.
Van Der Werf MJ , De Vlas SJ , Brooker S , Looman CWN , Nagelkerke NJD , Habbema JDF , Engels D , 2003. Quantification of clinical morbidity associated with schistosome infection in sub-Saharan Africa. Acta Trop 86: 125–139.
-
2.
Hotez PJ et al., 2014. The Global Burden of Disease Study 2010: interpretation and implications for the neglected tropical diseases. PLoS Negl Trop Dis 8.
-
3.
WHO , 2006. Preventive Chemotherapy in Human Helminthiasis. Geneva, Switzerland: World Health Organization. Available at: http://apps.who.int/iris/bitstream/handle/10665/43545/9241547103_eng.pdf?sequence=1.
-
4.
Corstjens PLAM et al., 2014. Tools for diagnosis, monitoring and screening of Schistosoma infections utilizing lateral-flow based assays and upconverting phosphor labels. Parasitology 141: 1841–1855.
-
5.
Obeng BB et al., 2008. Application of a circulating-cathodic-antigen (CCA) strip test and real-time PCR, in comparison with microscopy, for the detection of Schistosoma haematobium in urine samples from Ghana. Ann Trop Med Parasitol 102: 625–633.
-
6.
Midzi N , Butterworth AE , Mduluza T , Munyati S , Deelder AM , van Dam GJ , 2009. Use of circulating cathodic antigen strips for the diagnosis of urinary schistosomiasis. Trans R Soc Trop Med Hyg 103: 45–51.
-
7.
Dazo BC , Biles JE , 1974. Two new field techniques for detection and counting of Schistosoma haematobium eggs in urine samples, with an evaluation of both methods. Bull World Health Organ 51: 399–408.
-
8.
WHO, 2002. Prevention and Control of Schistosomiasis and Soil-Transmitted Helminthiasis: Report of a WHO Expert Committee. Geneva, Switzerland: World Health Organization. Available at: https://apps.who.int/iris/handle/10665/42588.
-
9.
Lier T , Simonsen GS , Wang T , Lu D , Haukland HH , Vennervald BJ , Johansen MV , 2009. Low sensitivity of the formol-ethyl acetate sedimentation concentration technique in low-intensity Schistosoma japonicum infections. PLoS Negl Trop Dis 3: 6–9. https://doi.org/10.1371/journal.pntd.0000386.
-
10.
Weller TH , Dammin GJ , 1945. An improved method of examination of feces for the diagnosis of intestinal schistosomiasis. Am J Clin Pathol 15: 496–500.
-
11.
Switz NA , D’Ambrosio MV , Fletcher DA , 2014. Low-cost mobile phone microscopy with a reversed mobile phone camera lens. PLoS One 9: e95330.
-
12.
Ephraim RKD , Cybulski JS , Duah E , Prakash M , D’Ambrosio MV , Fletcher DA , Keiser J , Andrews JR , Bogoch II , 2015. Diagnosis of Schistosoma haematobium infection with a mobile phone-mounted Foldscope and a reversed-lens CellScope in Ghana. Am J Trop Med Hyg 92: 1253–1256.
-
13.
D’Ambrosio MV et al., 2015. Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope. Sci Transl Med 7: 286re4.
-
14.
Kamgno J et al., 2017. A test-and-not-treat strategy for onchocerciasis in Loa loa–endemic areas. N Engl J Med 377: 2044–2052.
-
15.
Zhao ZQ , Zheng P , Xu ST , Wu X , 2019. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30: 3212–3232.
-
16.
Abadi M et al., 2016. TensorFlow: Large-scale Machine Learning on Heterogeneous Distributed Systems. Available at: http://arxiv.org/abs/1603.04467.
-
17.
Lin TY , Goyal P , Girshick R , He K , Dollar P , 2020. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42: 318–327.
-
18.
Howard AG , Zhu M , Chen B , Kalenichenko D , Wang W , Weyand T , Andreetto M , Adam H , 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Available at: http://arxiv.org/abs/1704.04861.
-
19.
Tan M , Pang R , Le QV , 2020. EfficientDet: scalable and efficient object detection. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR42600.2020.01079.
-
20.
Lin T-Y , Maire M , Belongie S , Hays J , Perona P , Ramanan D , Dollár P , Zitnick CL , 2014. Microsoft COCO: Common Objects in Context. In: Fleet D , Pajdla T , Schiele B , Tuytelaars T , eds. Computer Vision–ECCV 2014. Cham, Switzerland: Springer International Publishing, 740–755.
-
21.
He K , Zhang X , Ren S , Sun J , 2016. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision Pattern Recognition. Silver Spring, MD: IEEE Computer Society, 770–778. https://doi.org/10.1109/CVPR.2016.90.
-
22.
Wang W , Wang L , Liang YS , 2012. Susceptibility or resistance of praziquantel in human schistosomiasis: a review. Parasitol Res 111: 1871–1877.
-
23.
Bogoch II , Koydemir HC , Tseng D , Ephraim RKD , Duah E , Tee J , Andrews JR , Ozcan A , 2017. Evaluation of a mobile phone-based microscope for screening of Schistosoma haematobium infection in rural Ghana. Am J Trop Med Hyg 96: 1468–1471.
-
24.
Al-Shehri H , Koukounari A , Stanton MC , Adriko M , Arinaitwe M , Atuhaire A , Kabatereine NB , Stothard JR , 2018. Surveillance of intestinal schistosomiasis during control: a comparison of four diagnostic tests across five Ugandan primary schools in the Lake Albert region. Parasitology 145: 1715–1722.
-
25.
King CH , Bertsch D , 2013. Meta-analysis of urine heme dipstick diagnosis of Schistosoma haematobium infection, including low-prevalence and previously-treated populations. PLoS Negl Trop Dis 7. https://doi.org/10.1371/journal.pntd.0002431.
-
26.
Karanja DMS , Secor WE , Matete DO , Worrell CM , Montgomery SP , Ochola EA , Bartoces M , Mwinzi PNM , 2015. Cost analysis of tests for the detection of Schistosoma mansoni infection in children in western Kenya. Am J Trop Med Hyg 92: 1233–1239.
-
27.
Knopp S et al., 2015. Sensitivity and specificity of a urine circulating anodic antigen test for the diagnosis of Schistosoma haematobium in low endemic settings. PLoS Negl Trop Dis. https://doi.org/10.1371/journal.pntd.0003752.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 2659 | 550 | 25 |
| Full Text Views | 87 | 56 | 2 |
| PDF Downloads | 89 | 44 | 2 |
Point-of-Care Sample Preparation and Automated Quantitative Detection of Schistosoma haematobium Using Mobile Phone Microscopy
Maxim Armstrong
Maxim Armstrong
Department of Bioengineering, University of California, Berkeley, Berkeley, California;
Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California;
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Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California;
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Andrew R. Harris
Andrew R. Harris
Department of Bioengineering, University of California, Berkeley, Berkeley, California;
Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Ontario, Canada;
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Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Ontario, Canada;
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Michael V. D’Ambrosio
Michael V. D’Ambrosio
Department of Bioengineering, University of California, Berkeley, Berkeley, California;
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Jean T. Coulibaly
Jean T. Coulibaly
Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d’Ivoire;
Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland;
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Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland;
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Samuel Essien-Baidoo
Samuel Essien-Baidoo
Department of Medical Laboratory Science, University of Cape Coast, Cape Coast, Ghana;
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Richard K. D. Ephraim
Richard K. D. Ephraim
Department of Medical Laboratory Science, University of Cape Coast, Cape Coast, Ghana;
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Jason R. Andrews
Jason R. Andrews
Department of Medicine, Stanford University, Stanford, California;
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Isaac I. Bogoch
Isaac I. Bogoch
Department of Medicine, University of Toronto, Toronto, Ontario, Canada;
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Daniel A. Fletcher
Daniel A. Fletcher
Department of Bioengineering, University of California, Berkeley, Berkeley, California;
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, Berkeley, California;
Chan Zuckerberg Biohub, San Francisco, California
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Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, Berkeley, California;
Chan Zuckerberg Biohub, San Francisco, California
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ABSTRACT.
Schistosoma haematobium continues to pose a significant public health burden despite ongoing global control efforts. One of several barriers to sustained control (and ultimately elimination) is the lack of access to highly sensitive diagnostic or screening tools that are inexpensive, rapid, and can be used at the point of sample collection. Here, we report an automated point-of-care diagnostic based on mobile phone microscopy that rapidly images and identifies S. haematobium eggs in urine samples. Parasite eggs are filtered from urine within a specialized, inexpensive cartridge that is then automatically imaged by the mobile phone microscope (the “SchistoScope”). Parasite eggs are captured at a constriction point in the tapered cartridge for easy imaging, and the automated quantification of eggs is obtained upon analysis of the images by an algorithm. We demonstrate S. haematobium egg detection with greater than 90% sensitivity and specificity using this device compared with the field gold standard of conventional filtration and microscopy. With simple sample preparation and image analysis on a mobile phone, the SchistoScope combines the diagnostic performance of conventional microscopy with the analytic performance of an expert technician. This portable device has the potential to provide rapid and quantitative diagnosis of S. haematobium to advance ongoing control efforts.
Author Notes
Financial support: This work was supported by a generous gift from Mitsuru and Lucinda Igarashi, the Blum Center for Developing Economies at UC Berkeley through a grant from USAID, and donors to the Health Tech CoLab (DAF), as well as funding from an MSH-UHN AMO Innovation Funding grant and Ontario New Frontiers Grant (NFRFE/2020/00922).
Authors’ addresses: Maxim Armstrong, Michael V. D’Ambrosio, and Daniel A. Fletcher, University of California Berkeley, Berkeley, CA, E-mails: maxarmstrong@berkeley.edu, mdambrosio@berkeley.edu, and fletch@berkeley.edu. Andrew R. Harris and Isaac I. Bogoch, Carleteon University, Ottawa, ON, Canada, E-mails: andrewharris3@cunet.carleton.ca and isaac.bogoch@uhn.ca. Jean T. Coulibaly, Université Félix Houphouët-Boigny, Abidjan, Lagunes, Cote d’Ivoire, E-mail: jean.coulibaly@swisstph.ch. Samuel Essien-Baidoo, Richard K. D. Ephraim, University of Cape Coast, Cape Coast, Central, Ghana, E-mails: sessien-baidoo@ucc.edu.gh and rephraim@ucc.edu.gh. Jason R. Andrews, Stanford University, Stanford, CA, E-mail: jandr@stanford.edu.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Van Der Werf MJ , De Vlas SJ , Brooker S , Looman CWN , Nagelkerke NJD , Habbema JDF , Engels D , 2003. Quantification of clinical morbidity associated with schistosome infection in sub-Saharan Africa. Acta Trop 86: 125–139.
-
2.
Hotez PJ et al., 2014. The Global Burden of Disease Study 2010: interpretation and implications for the neglected tropical diseases. PLoS Negl Trop Dis 8.
-
3.
WHO , 2006. Preventive Chemotherapy in Human Helminthiasis. Geneva, Switzerland: World Health Organization. Available at: http://apps.who.int/iris/bitstream/handle/10665/43545/9241547103_eng.pdf?sequence=1.
-
4.
Corstjens PLAM et al., 2014. Tools for diagnosis, monitoring and screening of Schistosoma infections utilizing lateral-flow based assays and upconverting phosphor labels. Parasitology 141: 1841–1855.
-
5.
Obeng BB et al., 2008. Application of a circulating-cathodic-antigen (CCA) strip test and real-time PCR, in comparison with microscopy, for the detection of Schistosoma haematobium in urine samples from Ghana. Ann Trop Med Parasitol 102: 625–633.
-
6.
Midzi N , Butterworth AE , Mduluza T , Munyati S , Deelder AM , van Dam GJ , 2009. Use of circulating cathodic antigen strips for the diagnosis of urinary schistosomiasis. Trans R Soc Trop Med Hyg 103: 45–51.
-
7.
Dazo BC , Biles JE , 1974. Two new field techniques for detection and counting of Schistosoma haematobium eggs in urine samples, with an evaluation of both methods. Bull World Health Organ 51: 399–408.
-
8.
WHO, 2002. Prevention and Control of Schistosomiasis and Soil-Transmitted Helminthiasis: Report of a WHO Expert Committee. Geneva, Switzerland: World Health Organization. Available at: https://apps.who.int/iris/handle/10665/42588.
-
9.
Lier T , Simonsen GS , Wang T , Lu D , Haukland HH , Vennervald BJ , Johansen MV , 2009. Low sensitivity of the formol-ethyl acetate sedimentation concentration technique in low-intensity Schistosoma japonicum infections. PLoS Negl Trop Dis 3: 6–9. https://doi.org/10.1371/journal.pntd.0000386.
-
10.
Weller TH , Dammin GJ , 1945. An improved method of examination of feces for the diagnosis of intestinal schistosomiasis. Am J Clin Pathol 15: 496–500.
-
11.
Switz NA , D’Ambrosio MV , Fletcher DA , 2014. Low-cost mobile phone microscopy with a reversed mobile phone camera lens. PLoS One 9: e95330.
-
12.
Ephraim RKD , Cybulski JS , Duah E , Prakash M , D’Ambrosio MV , Fletcher DA , Keiser J , Andrews JR , Bogoch II , 2015. Diagnosis of Schistosoma haematobium infection with a mobile phone-mounted Foldscope and a reversed-lens CellScope in Ghana. Am J Trop Med Hyg 92: 1253–1256.
-
13.
D’Ambrosio MV et al., 2015. Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope. Sci Transl Med 7: 286re4.
-
14.
Kamgno J et al., 2017. A test-and-not-treat strategy for onchocerciasis in Loa loa–endemic areas. N Engl J Med 377: 2044–2052.
-
15.
Zhao ZQ , Zheng P , Xu ST , Wu X , 2019. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30: 3212–3232.
-
16.
Abadi M et al., 2016. TensorFlow: Large-scale Machine Learning on Heterogeneous Distributed Systems. Available at: http://arxiv.org/abs/1603.04467.
-
17.
Lin TY , Goyal P , Girshick R , He K , Dollar P , 2020. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42: 318–327.
-
18.
Howard AG , Zhu M , Chen B , Kalenichenko D , Wang W , Weyand T , Andreetto M , Adam H , 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Available at: http://arxiv.org/abs/1704.04861.
-
19.
Tan M , Pang R , Le QV , 2020. EfficientDet: scalable and efficient object detection. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR42600.2020.01079.
-
20.
Lin T-Y , Maire M , Belongie S , Hays J , Perona P , Ramanan D , Dollár P , Zitnick CL , 2014. Microsoft COCO: Common Objects in Context. In: Fleet D , Pajdla T , Schiele B , Tuytelaars T , eds. Computer Vision–ECCV 2014. Cham, Switzerland: Springer International Publishing, 740–755.
-
21.
He K , Zhang X , Ren S , Sun J , 2016. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision Pattern Recognition. Silver Spring, MD: IEEE Computer Society, 770–778. https://doi.org/10.1109/CVPR.2016.90.
-
22.
Wang W , Wang L , Liang YS , 2012. Susceptibility or resistance of praziquantel in human schistosomiasis: a review. Parasitol Res 111: 1871–1877.
-
23.
Bogoch II , Koydemir HC , Tseng D , Ephraim RKD , Duah E , Tee J , Andrews JR , Ozcan A , 2017. Evaluation of a mobile phone-based microscope for screening of Schistosoma haematobium infection in rural Ghana. Am J Trop Med Hyg 96: 1468–1471.
-
24.
Al-Shehri H , Koukounari A , Stanton MC , Adriko M , Arinaitwe M , Atuhaire A , Kabatereine NB , Stothard JR , 2018. Surveillance of intestinal schistosomiasis during control: a comparison of four diagnostic tests across five Ugandan primary schools in the Lake Albert region. Parasitology 145: 1715–1722.
-
25.
King CH , Bertsch D , 2013. Meta-analysis of urine heme dipstick diagnosis of Schistosoma haematobium infection, including low-prevalence and previously-treated populations. PLoS Negl Trop Dis 7. https://doi.org/10.1371/journal.pntd.0002431.
-
26.
Karanja DMS , Secor WE , Matete DO , Worrell CM , Montgomery SP , Ochola EA , Bartoces M , Mwinzi PNM , 2015. Cost analysis of tests for the detection of Schistosoma mansoni infection in children in western Kenya. Am J Trop Med Hyg 92: 1233–1239.
-
27.
Knopp S et al., 2015. Sensitivity and specificity of a urine circulating anodic antigen test for the diagnosis of Schistosoma haematobium in low endemic settings. PLoS Negl Trop Dis. https://doi.org/10.1371/journal.pntd.0003752.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 2659 | 550 | 25 |
| Full Text Views | 87 | 56 | 2 |
| PDF Downloads | 89 | 44 | 2 |