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Oyibo P, Agbana T, van Lieshout L, Oyibo W, Diehl JC, Vdovine G. An automated slide scanning system for membrane filter imaging in diagnosis of urogenital schistosomiasis. J Microsc 2024; 294:52-61. [PMID: 38291833 DOI: 10.1111/jmi.13269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/01/2024]
Abstract
Traditionally, automated slide scanning involves capturing a rectangular grid of field-of-view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images to determine the best in-focus image. However, these methods can be time-consuming due to the need for X-, Y- and Z-axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best in-focus FoV image, we utilise a hill-climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z-axis. We implemented this procedure to optimise the efficiency of the Schistoscope, an automated digital microscope developed to diagnose urogenital schistosomiasis by imaging Schistosoma haematobium eggs on 13 or 25 mm membrane filters. Our improved method reduces the automated slide scanning time by 63.18% and 72.52% for the respective filter sizes. This advancement greatly supports the practicality of the Schistoscope in large-scale schistosomiasis monitoring and evaluation programs in endemic regions. This will save time, resources and also accelerate generation of data that is critical in achieving the targets for schistosomiasis elimination.
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Affiliation(s)
- Prosper Oyibo
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Tope Agbana
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
| | - Lisette van Lieshout
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | - Wellington Oyibo
- Centre for Transdisciplinary Research for Malaria & Neglected Tropical Diseases, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Jan-Carel Diehl
- Department of Sustainable Design Engineering, Delft University of Technology, Delft, The Netherlands
| | - Gleb Vdovine
- Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands
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Meulah B, Oyibo P, Hoekstra PT, Moure PAN, Maloum MN, Laclong-Lontchi RA, Honkpehedji YJ, Bengtson M, Hokke C, Corstjens PLAM, Agbana T, Diehl JC, Adegnika AA, van Lieshout L. Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon. PLoS Negl Trop Dis 2024; 18:e0011967. [PMID: 38394298 PMCID: PMC10917302 DOI: 10.1371/journal.pntd.0011967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 03/06/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
INTRODUCTION Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA). METHODS Based on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium. RESULTS In study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively). CONCLUSION Overall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings. TRIAL REGISTRATION NCT04505046 ClinicalTrials.gov.
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Affiliation(s)
- Brice Meulah
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
- Centre de Recherches Médicales des Lambaréné, CERMEL, Lambaréné, Gabon
| | - Prosper Oyibo
- Mechanical, Maritime and Material Engineering, Delft University of Technology, Delft, The Netherlands
| | - Pytsje T. Hoekstra
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
| | - Paul Alvyn Nguema Moure
- Centre de Recherches Médicales des Lambaréné, CERMEL, Lambaréné, Gabon
- Ecole doctorale régionale d’Afrique centrale en infectiologie tropicale de Franceville, Gabon
| | | | | | - Yabo Josiane Honkpehedji
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
- Centre de Recherches Médicales des Lambaréné, CERMEL, Lambaréné, Gabon
- Fondation pour la Recherche Scientifique, Cotonou, Benin
| | - Michel Bengtson
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
| | - Cornelis Hokke
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
| | - Paul L. A. M. Corstjens
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - Temitope Agbana
- Mechanical, Maritime and Material Engineering, Delft University of Technology, Delft, The Netherlands
| | - Jan Carel Diehl
- Industrial Design Engineering, Delft University of Technology, Delft, The Netherlands
| | - Ayola Akim Adegnika
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
- Centre de Recherches Médicales des Lambaréné, CERMEL, Lambaréné, Gabon
- Ecole doctorale régionale d’Afrique centrale en infectiologie tropicale de Franceville, Gabon
- Fondation pour la Recherche Scientifique, Cotonou, Benin
- Institut fur Tropenmedizin, Universitat Tubingen, Tubingen, Germany
| | - Lisette van Lieshout
- Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center, Leiden, The Netherlands
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Oyibo P, Meulah B, Bengtson M, van Lieshout L, Oyibo W, Diehl JC, Vdovine G, Agbana T. Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings. J Med Imaging (Bellingham) 2023; 10:044005. [PMID: 37554627 PMCID: PMC10405291 DOI: 10.1117/1.jmi.10.4.044005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/23/2023] [Accepted: 07/21/2023] [Indexed: 08/10/2023] Open
Abstract
PURPOSE Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage diagnosis framework for urogenital schistosomiasis. APPROACH Urine samples obtained from field settings were captured using the Schistoscope device, and S. haematobium eggs present in the images were manually annotated by experts to create the SH dataset. Next, we develop a two-stage diagnosis framework, which consists of semantic segmentation of S. haematobium eggs using the DeepLabv3-MobileNetV3 deep convolutional neural network and a refined segmentation step using ellipse fitting approach to approximate the eggs with an automatically determined number of ellipses. We defined two linear inequality constraints as a function of the overlap coefficient and area of a fitted ellipses. False positive diagnosis resulting from over-segmentation was further minimized using these constraints. We evaluated the performance of our framework on 7605 images from 65 independent urine samples collected from field settings in Nigeria, by deploying our algorithm on an Edge AI system consisting of Raspberry Pi + Coral USB accelerator. RESULT The SH dataset contains 12,051 images from 103 independent urine samples and the developed urogenital schistosomiasis diagnosis framework achieved clinical sensitivity, specificity, and precision of 93.8%, 93.9%, and 93.8%, respectively, using results from an experienced microscopist as reference. CONCLUSION Our detection framework is a promising tool for the diagnosis of urogenital schistosomiasis as our results meet the World Health Organization target product profile requirements for monitoring and evaluation of schistosomiasis control programs.
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Affiliation(s)
- Prosper Oyibo
- Delft University of Technology, Delft Center for Systems and Control, Faculty of Mechanical, Maritime, and Materials Engineering, Delft, The Netherlands
- University of Lagos, College of Medicine, Centre for Malaria Diagnosis, NTD Research, Training, and Policy/ANDI Centre of Excellence for Malaria Diagnosis, Lagos, Nigeria
| | - Brice Meulah
- Leiden University Medical Centre, Department of Parasitology, Leiden, The Netherlands
- Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene, Gabon
| | - Michel Bengtson
- Leiden University Medical Centre, Department of Parasitology, Leiden, The Netherlands
| | - Lisette van Lieshout
- Leiden University Medical Centre, Department of Parasitology, Leiden, The Netherlands
| | - Wellington Oyibo
- University of Lagos, College of Medicine, Centre for Malaria Diagnosis, NTD Research, Training, and Policy/ANDI Centre of Excellence for Malaria Diagnosis, Lagos, Nigeria
| | - Jan-Carel Diehl
- Delft University of Technology, Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft, The Netherlands
| | - Gleb Vdovine
- Delft University of Technology, Delft Center for Systems and Control, Faculty of Mechanical, Maritime, and Materials Engineering, Delft, The Netherlands
| | - Tope Agbana
- Delft University of Technology, Delft Center for Systems and Control, Faculty of Mechanical, Maritime, and Materials Engineering, Delft, The Netherlands
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Meulah B, Oyibo P, Bengtson M, Agbana T, Lontchi RAL, Adegnika AA, Oyibo W, Hokke CH, Diehl JC, van Lieshout L. Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria. Am J Trop Med Hyg 2022; 107:1047-1054. [PMID: 36252803 DOI: 10.4269/ajtmh.22-0276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/03/2022] [Indexed: 11/07/2022] Open
Abstract
Conventional microscopy is the standard procedure for the diagnosis of schistosomiasis, despite its limited sensitivity, reliance on skilled personnel, and the fact that it is error prone. Here, we report the performance of the innovative (semi-)automated Schistoscope 5.0 for optical digital detection and quantification of Schistosoma haematobium eggs in urine, using conventional microscopy as the reference standard. At baseline, 487 participants in a rural setting in Nigeria were assessed, of which 166 (34.1%) tested S. haematobium positive by conventional microscopy. Captured images from the Schistoscope 5.0 were analyzed manually (semiautomation) and by an artificial intelligence (AI) algorithm (full automation). Semi- and fully automated digital microscopy showed comparable sensitivities of 80.1% (95% confidence interval [CI]: 73.2-86.0) and 87.3% (95% CI: 81.3-92.0), but a significant difference in specificity of 95.3% (95% CI: 92.4-97.4) and 48.9% (95% CI: 43.3-55.0), respectively. Overall, estimated egg counts of semi- and fully automated digital microscopy correlated significantly with the egg counts of conventional microscopy (r = 0.90 and r = 0.80, respectively, P < 0.001), although the fully automated procedure generally underestimated the higher egg counts. In 38 egg positive cases, an additional urine sample was examined 10 days after praziquantel treatment, showing a similar cure rate and egg reduction rate when comparing conventional microscopy with semiautomated digital microscopy. In this first extensive field evaluation, we found the semiautomated Schistoscope 5.0 to be a promising tool for the detection and monitoring of S. haematobium infection, although further improvement of the AI algorithm for full automation is required.
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Affiliation(s)
- Brice Meulah
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands.,Centre de Recherches Médicales des Lambaréné, CERMEL, Lambaréné, Gabon
| | - Prosper Oyibo
- Mechanical, Maritime and Material Engineering, Delft University of Technology, Delft, The Netherlands.,Centre for Malaria Diagnosis, NTD Research, Training & Policy/ANDI Centre of Excellence for Malaria Diagnosis, University of Lagos, Lagos, Nigeria
| | - Michel Bengtson
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | - Temitope Agbana
- Mechanical, Maritime and Material Engineering, Delft University of Technology, Delft, The Netherlands
| | | | - Ayola Akim Adegnika
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands.,Centre de Recherches Médicales des Lambaréné, CERMEL, Lambaréné, Gabon.,Institut fur Tropenmedizin, Universitat Tubingen, Tubingen, Germany.,German Center for Infection Research (DZIF), partner site Tübingen, Germany
| | - Wellington Oyibo
- Centre for Malaria Diagnosis, NTD Research, Training & Policy/ANDI Centre of Excellence for Malaria Diagnosis, University of Lagos, Lagos, Nigeria
| | | | - Jan Carel Diehl
- Industrial Design Engineering, Delft University of Technology, Delft, The Netherlands
| | - Lisette van Lieshout
- Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
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Oyibo P, Jujjavarapu S, Meulah B, Agbana T, Braakman I, van Diepen A, Bengtson M, van Lieshout L, Oyibo W, Vdovine G, Diehl JC. Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings. Micromachines 2022; 13:mi13050643. [PMID: 35630110 PMCID: PMC9146062 DOI: 10.3390/mi13050643] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 02/01/2023]
Abstract
For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.
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Affiliation(s)
- Prosper Oyibo
- Delft Center for Systems and Control, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (P.O.); (T.A.); (G.V.)
- ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos, Lagos 101017, Nigeria;
| | - Satyajith Jujjavarapu
- Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands; (S.J.); (I.B.)
| | - Brice Meulah
- Department of Parasitology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (B.M.); (A.v.D.); (M.B.); (L.v.L.)
- Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene BP 242, Gabon
| | - Tope Agbana
- Delft Center for Systems and Control, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (P.O.); (T.A.); (G.V.)
| | - Ingeborg Braakman
- Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands; (S.J.); (I.B.)
| | - Angela van Diepen
- Department of Parasitology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (B.M.); (A.v.D.); (M.B.); (L.v.L.)
| | - Michel Bengtson
- Department of Parasitology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (B.M.); (A.v.D.); (M.B.); (L.v.L.)
| | - Lisette van Lieshout
- Department of Parasitology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (B.M.); (A.v.D.); (M.B.); (L.v.L.)
| | - Wellington Oyibo
- ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos, Lagos 101017, Nigeria;
| | - Gleb Vdovine
- Delft Center for Systems and Control, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; (P.O.); (T.A.); (G.V.)
| | - Jan-Carel Diehl
- Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands; (S.J.); (I.B.)
- Correspondence: ; Tel.: +31-614-015-469
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