Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Oscar Holmström, Nina Linder, Billy Ngasala, Andreas Mårtensson, Ewert Linder, Mikael Lundin, Hannu Moilanen, Antti Suutala, Vinod Diwan, Johan Lundin, Oscar Holmström, Nina Linder, Billy Ngasala, Andreas Mårtensson, Ewert Linder, Mikael Lundin, Hannu Moilanen, Antti Suutala, Vinod Diwan, Johan Lundin

Abstract

Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis.

Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images.

Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs.

Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3-100%) in the test set (n = 217) of manually labeled helminth eggs.

Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.

Keywords: Neglected tropical diseases; computer vision; global health; helminth; mHealth for Improved Access and Equity in Health Care; point-of-care.

Conflict of interest statement

Johan Lundin and Mikael Lundin are founders and co-owners of Fimmic Oy, Helsinki, Finland.

Figures

Figure 1.
Figure 1.
MoMic digital microscope scanner (1) with external motor unit attached (2). The microscope glass (3) is placed in the slide holder (4), which is placed in the microscope and navigated from the motor unit. The device is connected to and operated from a laptop computer (5) running software (6) for operation of the device.
Figure 2.
Figure 2.
Region of glass slide captured with the mobile microscope. Enlarged areas (right) showing A. lumbricoides eggs in sample (magnified images showing native, full resolution of captured images at 100% digital magnification).
Figure 3.
Figure 3.
Stool sample showing A. lumbricoides eggs, digitized with (a) mobile microscope, and (b) reference slide-scanner.
Figure 4.
Figure 4.
Captured images with the mobile microscopes with visible parasites. Enlarged areas showing the parasites at 300% digital zoom. (a) A. lumbricoides, (b) T. trichiura, (c) hookworm, (d) S. haematobium.
Figure 5.
Figure 5.
Results of digital image analysis of fixated stool sample (A. lumbricoides infection), showing detected regions of interest of fixated stool sample, sorted by certainty of representing parasite (descending order, from most likely candidates to least likely), as detected by the image analysis software.

References

    1. Hotez PJ, Molyneux DH, Fenwick A, et al. Control of neglected tropical diseases. N Engl J Med. 2007;357:1018–57.
    1. Molyneux DH, Hotez PJ, Fenwick A.. “Rapid-impact interventions”: how a policy of integrated control for Africa’s neglected tropical diseases could benefit the poor. PLoS Med. 2005;2:e336.
    1. Brooker S, Clements ACA, Bundy DAP.. Global epidemiology, ecology and control of soil-transmitted helminth infections. Adv Parasitol. 2006;62:221–261.
    1. Hotez PJ, Molyneux DH, Fenwick A, et al. Incorporating a rapid-impact package for neglected tropical diseases with programs for HIV/AIDS, tuberculosis, and malaria. PLoS Med. 2006;3:e102.
    1. Crompton DWT, Nesheim MC. Nutritional impact of intestinal helminthiasis during the human life cycle. Annu Rev Nutr. 2002;22:35–59.
    1. Christian P, Khatry SK, West KPJ. Antenatal anthelmintic treatment, birthweight, and infant survival in rural Nepal. Lancet. 2004;364:981–983.
    1. Brooker S, Hotez PJ, Bundy DAP. Hookworm-related anaemia among pregnant women: a systematic review. PLoS Negl Trop Dis. 2008;2:e291.
    1. Knopp S, Steinmann P, Keiser J, et al. Nematode infections: soil-transmitted helminths and trichinella. Infect Dis Clin North Am. 2012;26:341–358.
    1. Knopp S, Mgeni AF, Khamis IS, et al. Diagnosis of soil-transmitted helminths in the era of preventive chemotherapy: effect of multiple stool sampling and use of different diagnostic techniques. PLoS Negl Trop Dis. 2008;2:e331.
    1. Knopp S, Becker SL, Ingram KJ, et al. Diagnosis and treatment of schistosomiasis in children in the era of intensified control. Expert Rev Antiinfect Ther. 2013;11:1237–1258.
    1. Petti CA, Polage CR, Quinn TC, et al. Laboratory medicine in Africa: a barrier to effective health care. Clin Infect Dis. 2006;42:377–382.
    1. Bethony J, Brooker S, Albonico M, et al. Soil-transmitted helminth infections: ascariasis, trichuriasis, and hookworm. Lancet. 2006;367:1521–1532.
    1. Speich B, Knopp S, Mohammed KA, et al. Comparative cost assessment of the Kato-Katz and FLOTAC techniques for soil-transmitted helminth diagnosis in epidemiological surveys. Parasit Vectors. 2010;3:71.
    1. Bogoch II, Andrews JR, Speich B, et al. Quantitative evaluation of a handheld light microscope for field diagnosis of soil-transmitted helminth infection. Am J Trop Med Hyg. 2014;91:1138–1141.
    1. Bogoch II, Andrews JR, Speich B, et al. Mobile phone microscopy for the diagnosis of soil-transmitted helminth infections: a proof-of-concept study. Am J Trop Med Hyg. 2013;88:626–629.
    1. Bogoch II, Coulibaly JT, Andrews JR, et al. Evaluation of portable microscopic devices for the diagnosis of Schistosoma and soil-transmitted helminth infection. Parasitology. 2014;141:1811–1818.
    1. Ephraim RKD, Duah E, Cybulski JS, et al. Diagnosis of Schistosoma haematobium infection with a mobile phone-mounted Foldscope and a reversed-lens CellScope in Ghana. Am J Trop Med Hyg. 2015;92:1253–1256.
    1. Switz NA, D’Ambrosio MV, Fletcher DA. Low-cost mobile phone microscopy with a reversed mobile phone camera lens. PLoS One. 2014;9:e95330.
    1. Smith ZJ, Chu K, Espenson AR, et al. Cell-phone-based platform for biomedical device development and education applications. PLoS One. 2011;6:e17150.
    1. Lee M, Yaglidere O, Ozcan A. Field-portable reflection and transmission microscopy based on lensless holography. Biomed Opt Express. 2011;2:2721–2730.
    1. Greenbaum A, Sikora U, Ozcan A. Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging. Lab Chip. 2012;12:1242–1245.
    1. Isikman SO, Greenbaum A, Lee M, et al. Lensfree computational microscopy tools for cell and tissue imaging at the point-of-care and in low-resource settings. Stud Health Technol Inf. 2013;185:299–323.
    1. Greenbaum A, Luo W, Su T, et al. Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy. Nat Methods. 2012;9:889–895.
    1. McLeod E, Luo W, Mudanyali O, et al. Toward giga-pixel nanoscopy on a chip: a computational wide-field look at the nano-scale without the use of lenses. Lab Chip. 2013;13:2028–2035.
    1. Tuijn CJ, Hoefman BJ, van Beijma H, et al. Data and image transfer using mobile phones to strengthen microscopy-based diagnostic services in low and middle income country laboratories. PLoS One. 2011;6:e28348.
    1. Frean J. Microscopic images transmitted by mobile cameraphone. Trans R Soc Trop Med Hyg. 2007;101:1053.
    1. Linder E, Grote A, Varjo S, et al. On-chip imaging of Schistosoma haematobium eggs in urine for diagnosis by computer vision. PLoS Negl Trop Dis. 2013;7:e2547.
    1. Cornish TC, Swapp RE, Kaplan KJ. Whole-slide imaging: routine pathologic diagnosis. Adv Anat Pathol. 2012;19:152–159.
    1. Holmström O, Linder N, Lundin M, et al. Quantification of estrogen receptor-alpha expression in human breast carcinomas with a miniaturized, low-cost digital microscope: a comparison with a high-end whole slide-scanner. PLoS One. 2015;10:e0144688.
    1. Konsti J, Lundin M, Linder N, et al. Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium. Diagn Pathol. 2012;7:29.
    1. Jiménez B, Maya C, Velásquez G, et al. Identification and quantification of pathogenic helminth eggs using a digital image system. Exp Parasitol. 2016;166:164–172.
    1. Suzuki CTN, Gomes JF, Falcão AX, et al. Automatic segmentation and classification of human intestinal parasites from microscopy images. IEEE Trans Biomed Eng. 2013;60:803–812.
    1. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.
    1. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410.
    1. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–118.
    1. Rojo MG, García GB, Mateos CP, et al. Critical comparison of 31 commercially available digital slide systems in pathology. Int J Surg Pathol. 2006;14:285–305.
    1. McCarthy JS, Lustigman S, Yang G, et al. A research agenda for helminth diseases of humans: diagnostics for control and elimination programmes. PLoS Negl Trop Dis. 2012;6:e1601.
    1. Boppart SA, Richards-Kortum R. Point-of-care and point-of-procedure optical imaging technologies for primary care and global health. Sci Transl Med. 2014;6:253r2.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir