Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings

Beck, H. P.

doi:10.3389/frai.2022.510483

Cited by 6 publications

(6 citation statements)

References 43 publications

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“…Moreover, by modifying the composition of the hydrogels in the cartridge, the miLab TM can prepare different types of sample slides. These include cytology slides with Papanicolaou staining (Supplementary Figure 3B), or tissue sections stained with hematoxylin and eosin (H&E) for histopathology (Supplementary Figure 3C) (Chin et al, 2022). The miLab TM might also be applied to the diagnosis of other infections and detect intestinal parasites (Supplementary Figure 3D) that require microscopic examination without staining ('wet' preparations).…”

Section: Discussionmentioning

confidence: 99%

“…The majority of malaria cases (exceeding 99%) are concentrated in low- and middle-income nations, with sub-Saharan Africa accounting for 95% of cases. Despite cost-effective rapid diagnostic tests (RDTs) and precise polymerase chain reaction (PCR), manual microscopy coupled with visual inspection by highly trained experts is widely used for malaria diagnosis because of its robustness and limited false positives and false negatives (Beck, 2022; Fitri et al, 2022). However, smearing and staining quality as well as the expertise of microscopists who read smeared blood slides considerably affect the quality of microscopy-based diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the advantages offered by cost-effective Rapid Diagnostic Tests (RDTs) and the precision of Polymerase Chain Reaction (PCR), malaria diagnosis has traditionally relied on manual microscopy, coupled with visual inspection by highly trained experts. Manual microscopic examination is recommended because of its robustness in eliminating false-positive or false-negative results(Beck, 2022; Fitri et al, 2022). However, the quality of microscopy-based diagnosis is largely affected by the smearing and staining quality, and the expertise of microscopist who reads the smeared blood slides.…”

Section: Introductionmentioning

confidence: 99%

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Embedded deep-learning based sample-to-answer device for on-site malaria diagnosis

Bae,

Shin,

Kim

et al. 2023

Preprint

Self Cite

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In response to the ongoing global health problem caused by malaria, especially in resource-limited settings, digital microscopy must be improved to overcome the limitations associated with manual microscopy. In order to present a malaria diagnosis method that is not only accurate at the cell level but also clinically performs well, improvements in deep-learning algorithms and consistent staining results are necessary. The device employs a solid hydrogel staining method for consistent blood film preparation, eliminating the need for complex equipment and liquid reagent maintenance. By leveraging deformable staining patches, the miLab™ ensures consistent, high-quality reproducible blood films can be made across various hematocrits. Embedded deep-learning enables the miLab™ to detect and classify malaria parasites from the autofocused images of stained blood cells by internal optical system, achieving a high correlation with manual microscopy images. This innovation not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission, revolutionizing the landscape of on-site malaria diagnosis. The miLab™ algorithm for malaria detection shows a total accuracy of 98.83% for infected RBC classification. Clinical validation in Malawi demonstrates an overall percent agreement of 92.21%, highlighting the miLab™’s potential as a reliable and efficient tool for decentralized malaria diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Embedded deep-learning based sample-to-answer device for on-site malaria diagnosis

Bae,

Shin,

Kim

et al. 2023

Preprint

Self Cite

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“…Özellikle son yıllarda Derin Öğrenme (DL) ve Makine Öğrenme (ML) teknikleri, sıtma tanısı alanında büyük bir potansiyel sunmaktadır. Bu teknolojiler, büyük veri kümelerini işleme, karmaşık desenleri tanıma ve hızlı bir şekilde tanı koyma yetenekleri sayesinde sıtma tanısı için büyük imkân sağlamaktadırlar [8,9]. Tespit ve teşhis araçları doktorlara değerli bir ikinci görüş sunmakta ve tarama sürecinde onlara yardımcı olmaktadır [10].…”

Section: Introductionunclassified

Evrişimli Sinir Ağı (ESA) Mimarileri ile Hücre Görüntülerinden Sıtmanın Tespit Edilmesi

Özüpak

2024

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Sıtma, dünyanın birçok bölgesinde yaygın olarak görülen enfekte sivrisineklerin ısırıkları yoluyla insanlara bulaşan parazitlerin neden olduğu hayatı tehdit eden bir hastalıktır. Plasmodium adlı kan paraziti bu hastalığına sebep olmaktadır. Sıtmanın erken teşhisi ve tedavisi, özellikle hastalığın yaygın olduğu gelişmekte olan ülkelerde, hastalık ve ölüm oranlarının azaltılması açısından çok önemlidir. Sıtma teşhisinde kullanılan klasik yöntem, uzmanlar tarafından kırmızı kan hücrelerinin mikroskop yardımıyla incelenmesiyle tespitidir. Bu yöntem, sadece uzmanın bilgi ve deneyimine dayandığı için verimsizdir. Günümüzde hastalığın yüksek oranda doğru bir şekilde tespiti için makine öğrenmesi yöntemleri kullanılmaktadır. Bu çalışmada, hücreyi parazitli veya parazitsiz olarak tespit için Evrişimli Sinir Ağı (ESA) mimarisi önerilmiştir. Önerilen ESA mimarisine ek olarak VGG-19, InceptionResNetV2, DenseNet121 ve EfficientNetB3 gibi önceden eğitilmiş ESA mimarilerinin performansları ile önerdiğimiz modelin performansı karşılaştırılmıştır. Önerdiğimiz ESA mimarisinde National Institute of Health (NIH) tarafından yayınlanan Sıtma Veri Kümesi kullanılarak deneyler gerçekleştirilmiştir. Mimarimiz %98,9 doğruluk ile çalışmaktadır. Çalışmanın sonuçları, Plasmodium içeren hücre görüntülerinin doğruluğunu artırmada etkili olduğunu göstermektedir.

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“…Using this method, false-positive results can be obtained, which may be due to the persistence of the parasite antigen after successful treatment. However, RDTs, where false-negative results can also be observed, can create more serious problems [10].…”

Section: Introductionmentioning

confidence: 99%

Ensembling Object Detection Models for Robust and Reliable Malaria Parasite Detection in Thin Blood Smear Microscopic Images

Özbilge,

Güler,

Ozbilge

2024

IEEE Access

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Malaria is a blood disease caused by the Plasmodium parasite that is transmitted through the bite of female Anopheles mosquitoes. These mosquitoes can cross borders without passports or visas, making malaria a global health concern. To effectively treat malaria, infectious disease specialists must monitor the efficacy of the treatment by counting the number of parasites in a patient's blood at various time intervals. However, this task is challenging because it involves examining thin or thick blood smear samples under a microscope, which can be tiring to the human eye, particularly when there are many infected patients and a shortage of clinical experts. In such cases, rapid diagnosis is crucial. One approach is to capture microscopic images of blood smear samples using a camera and then employ deep learning-based object detection models to detect and count the infected red blood cells. In this study, state-of-the-art object detection models, including CenterNet, EfficientDet, Faster R-CNN, RetinaNet, and YOLOv8, were explored. The dataset was generated using thin blood smear images in the laboratory. The results revealed that YOLOv8s outperformed the other models, achieving

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Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings

Abstract: Beck H-P () Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings.

Cited by 6 publications

References 43 publications

Embedded deep-learning based sample-to-answer device for on-site malaria diagnosis

Embedded deep-learning based sample-to-answer device for on-site malaria diagnosis

Evrişimli Sinir Ağı (ESA) Mimarileri ile Hücre Görüntülerinden Sıtmanın Tespit Edilmesi

Ensembling Object Detection Models for Robust and Reliable Malaria Parasite Detection in Thin Blood Smear Microscopic Images

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