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Hamid MMA, Mohamed AO, Mohammed FO, Elaagip A, Mustafa SA, Elfaki T, Jebreel WMA, Albsheer MM, Dittrich S, Owusu EDA, Yerlikaya S. Diagnostic accuracy of an automated microscope solution (miLab™) in detecting malaria parasites in symptomatic patients at point-of-care in Sudan: a case-control study. Malar J 2024; 23:200. [PMID: 38943203 PMCID: PMC11212432 DOI: 10.1186/s12936-024-05029-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/26/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Microscopic detection of malaria parasites is labour-intensive, time-consuming, and expertise-demanding. Moreover, the slide interpretation is highly dependent on the staining technique and the technician's expertise. Therefore, there is a growing interest in next-generation, fully- or semi-integrated microscopes that can improve slide preparation and examination. This study aimed to evaluate the clinical performance of miLab™ (Noul Inc., Republic of Korea), a fully-integrated automated microscopy device for the detection of malaria parasites in symptomatic patients at point-of-care in Sudan. METHODS This was a prospective, case-control diagnostic accuracy study conducted in primary health care facilities in rural Khartoum, Sudan in 2020. According to the outcomes of routine on-site microscopy testing, 100 malaria-positive and 90 malaria-negative patients who presented at the health facility and were 5 years of age or older were enrolled consecutively. All consenting patients underwent miLab™ testing and received a negative or suspected result. For the primary analysis, the suspected results were regarded as positive (automated mode). For the secondary analysis, the operator reviewed the suspected results and categorized them as either negative or positive (corrected mode). Nested polymerase chain reaction (PCR) was used as the reference standard, and expert light microscopy as the comparator. RESULTS Out of the 190 patients, malaria diagnosis was confirmed by PCR in 112 and excluded in 78. The sensitivity of miLab™ was 91.1% (95% confidence interval [CI] 84.2-95.6%) and the specificity was 66.7% (95% Cl 55.1-67.7%) in the automated mode. The specificity increased to 96.2% (95% Cl 89.6-99.2%), with operator intervention in the corrected mode. Concordance of miLab with expert microscopy was substantial (kappa 0.65 [95% CI 0.54-0.76]) in the automated mode, but almost perfect (kappa 0.97 [95% CI 0.95-0.99]) in the corrected mode. A mean difference of 0.359 was found in the Bland-Altman analysis of the agreement between expert microscopy and miLab™ for quantifying parasite counts. CONCLUSION When used in a clinical context, miLab™ demonstrated high sensitivity but low specificity. Expert intervention was shown to be required to improve the device's specificity in its current version. miLab™ in the corrected mode performed similar to expert microscopy. Before clinical application, more refinement is needed to ensure full workflow automation and eliminate human intervention. Trial registration ClinicalTrials.gov: NCT04558515.
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Affiliation(s)
- Muzamil M Abdel Hamid
- Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan.
| | - Abdelrahim O Mohamed
- Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan
- Department of Biochemistry, Faculty of Medicine, University of Khartoum, Khartoum, Sudan
| | - Fayad O Mohammed
- Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan
| | - Arwa Elaagip
- Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan
| | - Sayed A Mustafa
- Malaria Control Program, Federal Ministry of Health, Khartoum, Sudan
| | - Tarig Elfaki
- Malaria Control Program, Federal Ministry of Health, Khartoum, Sudan
| | - Waleed M A Jebreel
- Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan
| | - Musab M Albsheer
- Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan
- Faculty of Medical Laboratory Sciences, Sinnar University, Sinnar, Sudan
| | | | - Ewurama D A Owusu
- FIND, Geneva, Switzerland
- Department of Medical Laboratory Sciences, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana
| | - Seda Yerlikaya
- FIND, Geneva, Switzerland
- Department of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, 69120, Heidelberg, Germany
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Dantas de Oliveira A, Rubio Maturana C, Zarzuela Serrat F, Carvalho BM, Sulleiro E, Prats C, Veiga A, Bosch M, Zulueta J, Abelló A, Sayrol E, Joseph-Munné J, López-Codina D. Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system. PLoS One 2024; 19:e0304085. [PMID: 38905190 PMCID: PMC11192333 DOI: 10.1371/journal.pone.0304085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/07/2024] [Indexed: 06/23/2024] Open
Abstract
In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system's components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide.
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Affiliation(s)
- Allisson Dantas de Oliveira
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Carles Rubio Maturana
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
- Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Francesc Zarzuela Serrat
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Bruno Motta Carvalho
- Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Elena Sulleiro
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
- Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
- CIBERINFEC, ISCIII- CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain
| | - Clara Prats
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
| | | | | | | | - Alberto Abelló
- Database Technologies and Information Group, Service and Information Systems Engineering Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Elisa Sayrol
- Tecnocampus, Universitat Pompeu Fabra, Mataró, Spain
| | - Joan Joseph-Munné
- Microbiology Department, Vall d’Hebron Research Institute (VHIR), Vall d’Hebron University Hospital, Barcelona, Spain
| | - Daniel López-Codina
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
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Yanik S, Yu H, Chaiyawong N, Adewale-Fasoro O, Dinis LR, Narayanasamy RK, Lee EC, Lubonja A, Li B, Jaeger S, Srinivasan P. Application of machine learning in a rodent malaria model for rapid, accurate, and consistent parasite counts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597554. [PMID: 38895284 PMCID: PMC11185661 DOI: 10.1101/2024.06.05.597554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can be inconsistent between individuals and labs. We have developed an easy-to-use machine learning (ML)-based software, Malaria Screener R, to expedite and standardize such studies by automating the counting of Plasmodium iRBCs in rodents. This software can process Giemsa-stained blood smear images captured by any camera-equipped microscope. It features an intuitive graphical user interface that facilitates image processing and visualization of the results. The software has been developed as a desktop application that processes images on standard Windows and Mac OS computers. A previous ML model created by the authors designed to count P. falciparum -infected human RBCs did not perform well counting Plasmodium -infected mouse RBCs. We leveraged that model by loading the pre-trained weights and training the algorithm with newly collected data to target P. yoelii and P. berghei mouse iRBCs. This new model reliably measured both P. yoelii and P. berghei parasitemia (R 2 = 0.9916). Additional rounds of training data to incorporate variances due to length of Giemsa staining, microscopes etc, have produced a generalizable model, meeting WHO Competency Level 1 for the sub-category of parasite counting using independent microscopes. Reliable, automated analyses of blood-stage parasitemia will facilitate rapid and consistent evaluation of novel vaccines and antimalarials across labs in an easily accessible in vivo malaria model.
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Balerdi-Sarasola L, Pedro F, Bottieau E, Genton B, Petrone P, Muñoz J, Camprubí-Ferrer D. MALrisk: a machine-learning–based tool to predict imported malaria in returned travellers with fever. J Travel Med 2024:taae054. [PMID: 38578987 DOI: 10.1093/jtm/taae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/13/2024] [Accepted: 04/03/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Early diagnosis is key to reducing the morbi-mortality associated with P. falciparum malaria among international travellers. However, access to microbiological tests can be challenging for some healthcare settings. Artificial Intelligence could improve the management of febrile travellers. METHODS Data from a multicentric prospective study of febrile travellers was obtained to build a machine-learning model to predict malaria cases among travellers presenting with fever. Demographic characteristics, clinical and laboratory variables were leveraged as features. Eleven machine-learning classification models were evaluated by 50-fold cross-validation in a Training set. Then, the model with the best performance, defined by the Area Under the Curve (AUC), was chosen for parameter optimization and evaluation in the Test set. Finally, a reduced model was elaborated with those features that contributed most to the model. RESULTS Out of eleven machine-learning models, XGBoost presented the best performance (mean AUC of 0.98 and a mean F1 score of 0.78). A reduced model (MALrisk) was developed using only six features: Africa as a travel destination, platelet count, rash, respiratory symptoms, hyperbilirubinemia and chemoprophylaxis intake. MALrisk predicted malaria cases with 100% (95%CI 96-100) sensitivity and 72% (95%CI 68-75) specificity. CONCLUSIONS The MALrisk can aid in the timely identification of malaria in non-endemic settings, allowing the initiation of empiric antimalarials and reinforcing the need for urgent transfer in healthcare facilities with no access to malaria diagnostic tests. This resource could be easily scalable to a digital application and could reduce the morbidity associated with late diagnosis.
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Affiliation(s)
| | - Fleitas Pedro
- ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Emmanuel Bottieau
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - Blaise Genton
- Center for Primary Care and Public Health, University of Lausanne, Switzerland
| | - Paula Petrone
- ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
| | - Jose Muñoz
- ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
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Guemas E, Routier B, Ghelfenstein-Ferreira T, Cordier C, Hartuis S, Marion B, Bertout S, Varlet-Marie E, Costa D, Pasquier G. Automatic patient-level recognition of four Plasmodium species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation. Microbiol Spectr 2024; 12:e0144023. [PMID: 38171008 PMCID: PMC10846087 DOI: 10.1128/spectrum.01440-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/15/2023] [Indexed: 01/05/2024] Open
Abstract
Malaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. The algorithm was trained and validated on a data set consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test data set of 4,508 images from 170 smears corresponding to 358,825 labels coming from six French university hospitals. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium-positive smears, the global accuracy was 82.7% (91/110) with a recall of 90% (38/42), 81.8% (18/22), and 76.1% (35/46) for P. falciparum, P. malariae, and P. ovale/vivax, respectively. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas lacking microscopy experts.IMPORTANCEMalaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. Performances were calculated with a test data set of 4,508 images from 170 smears coming from six French university hospitals. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices and could be suitable for deployment in low-resource setting areas.
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Affiliation(s)
- Emilie Guemas
- Department of Parasitology and Mycology, Academic Hospital (CHU) of Toulouse, Toulouse, France
- Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), CNRS UMR5051, INSERM UMR1291, UPS, Toulouse, France
| | - Baptiste Routier
- Laboratory of Parasitology-Mycology, EA7510 ESCAPE, University Hospital of Rouen, University of Rouen Normandie, Normandie, France
| | - Théo Ghelfenstein-Ferreira
- Université de Paris Cité, Laboratoire de Parasitologie-Mycologie, Groupe Hospitalier Saint-Louis-Lariboisière-Fernand-Widal, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Camille Cordier
- Laboratory of Parasitology-Mycology, INSERM U1285, Unité de Glycobiologie Structurale et Fonctionnelle (CNRS UMR 8576), University Hospital (CHU) of Lille, University of Lille, Lille, France
| | - Sophie Hartuis
- Nantes University,Academic Hospital (CHU) of Nantes,Cibles et Médicaments des Infections et de l'Immunité, IICiMed, UR1155, Nantes, France
| | - Bénédicte Marion
- Department of Physical Chemistry and Biophysics, Academic Hospital (CHU) of Montpellier, University of Montpellier, National Reference Centre (CNR) for Paludism, Montpellier, France
- Department of Parasitology/Mycology, Academic Hospital (CHU) of Montpellier, University of Montpellier, National Reference Centre (CNR) for Paludism, Montpellier, France
| | - Sébastien Bertout
- Laboratory of Parasitology/Mycology, UMI 233 TransVIHMI, University of Montpellier, IRD, INSERM U1175, Montpellier, France
| | - Emmanuelle Varlet-Marie
- Department of Physical Chemistry and Biophysics, Academic Hospital (CHU) of Montpellier, University of Montpellier, National Reference Centre (CNR) for Paludism, Montpellier, France
- Department of Parasitology/Mycology, Academic Hospital (CHU) of Montpellier, University of Montpellier, National Reference Centre (CNR) for Paludism, Montpellier, France
| | - Damien Costa
- Laboratory of Parasitology-Mycology, EA7510 ESCAPE, University Hospital of Rouen, University of Rouen Normandie, Normandie, France
| | - Grégoire Pasquier
- Department of Parasitology/Mycology, Academic Hospital (CHU) of Montpellier, University of Montpellier, National Reference Centre (CNR) for Paludism, Montpellier, France
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Amin J, Almas Anjum M, Ahmad A, Sharif MI, Kadry S, Kim J. Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization. PeerJ Comput Sci 2024; 10:e1744. [PMID: 38196949 PMCID: PMC10773915 DOI: 10.7717/peerj-cs.1744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 11/16/2023] [Indexed: 01/11/2024]
Abstract
Malaria disease can indeed be fatal if not identified and treated promptly. Due to advancements in the malaria diagnostic process, microscopy techniques are employed for blood cell analysis. Unfortunately, the diagnostic process of malaria via microscopy depends on microscopic skills. To overcome such issues, machine/deep learning algorithms can be proposed for more accurate and efficient detection of malaria. Therefore, a method is proposed for classifying malaria parasites that consist of three phases. The bilateral filter is applied to enhance image quality. After that shape-based and deep features are extracted. In shape-based pyramid histograms of oriented gradients (PHOG) features are derived with the dimension of N × 300. Deep features are derived from the residual network (ResNet)-50, and ResNet-18 at fully connected layers having the dimension of N × 1,000 respectively. The features obtained are fused serially, resulting in a dimensionality of N × 2,300. From this set, N × 498 features are chosen using the generalized normal distribution optimization (GNDO) method. The proposed method is accessed on a microscopic malarial parasite imaging dataset providing 99% classification accuracy which is better than as compared to recently published work.
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Affiliation(s)
- Javeria Amin
- University of Wah, Department of Computer Science, Wah Cantt, Pakistan
| | | | - Abraz Ahmad
- University of Wah, Department of Computer Science, Wah Cantt, Pakistan
| | - Muhammad Irfan Sharif
- Department of Information Sciences, University of Education Lahore, Jauharabad Campus, Jauharabad, Pakistan
| | - Seifedine Kadry
- Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE
- MEU Research Unit, Middle East University, Amman, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan, Korea
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Ojurongbe TA, Afolabi HA, Bashiru KA, Sule WF, Akinde SB, Ojurongbe O, Adegoke NA. Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment. Trop Dis Travel Med Vaccines 2023; 9:24. [PMID: 38098124 PMCID: PMC10722830 DOI: 10.1186/s40794-023-00208-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/28/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum (Pf) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features. METHOD Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively. RESULTS Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75-93%) and test set (AUC = 83%; 95% CI: 63-100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006). CONCLUSION Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making.
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Affiliation(s)
| | | | | | | | | | - Olusola Ojurongbe
- Department of Medical Microbiology and Parasitology, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
- Center for Emerging and Re-emerging Infectious Diseases, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Nurudeen A Adegoke
- Melanoma Institute Australia, The University of Sydney, Sydney, Australia
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Wang G, Luo G, Lian H, Chen L, Wu W, Liu H. Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears. Open Forum Infect Dis 2023; 10:ofad469. [PMID: 37937045 PMCID: PMC10627339 DOI: 10.1093/ofid/ofad469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/13/2023] [Indexed: 11/09/2023] Open
Abstract
Background Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical prospect. Methods You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation metrics. Results The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites. Conclusions The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.
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Affiliation(s)
- Geng Wang
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, China
| | - Guoju Luo
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, China
| | - Heqing Lian
- Beijing Xiaoying Technology Co, Ltd, Beijing, China
| | - Lei Chen
- Beijing Xiaoying Technology Co, Ltd, Beijing, China
| | - Wei Wu
- Department of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, China
| | - Hui Liu
- Central Laboratory, Yunnan Institute of Parasite Diseases, Puer, China
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Zhong Y, Dan Y, Cai Y, Lin J, Huang X, Mahmoud O, Hald ES, Kumar A, Fang Q, Mahmoud SS. Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:226-233. [PMID: 38059069 PMCID: PMC10697288 DOI: 10.1109/ojemb.2023.3328435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 12/08/2023] Open
Abstract
Goal: The purpose of this work is to improve malaria diagnosis efficiency by integrating smartphones with microscopes. This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. Methods: This approach combines image segmentation and a convolutional neural network (CNN) to distinguish between white blood cells, artifacts, and malaria parasites. A portable system integrates a microscope with a graphical user interface to facilitate rapid malaria detection from smartphone images. We trained the CNN model using open-source data from the Chittagong Medical College Hospital, Bangladesh. Results: The validation process, using microscopic TBS from both the training dataset and an additional dataset from Sub-Saharan Africa, demonstrated that the proposed model achieved an accuracy of 97.74% ± 0.05% and an F1-score of 97.75% ± 0.04%. Remarkably, our proposed model with AlexNet surpasses the reported literature performance of 96.32%. Conclusions: This algorithm shows promise in aiding malaria-stricken regions, especially those with limited resources.
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Affiliation(s)
- Yuming Zhong
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Ying Dan
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Yin Cai
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Jiamin Lin
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Xiaoyao Huang
- Shantou University Medical CollegeShantou UniversityShantou515063China
| | | | - Eric S. Hald
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Akshay Kumar
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Qiang Fang
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Seedahmed S. Mahmoud
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
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10
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Liu R, Liu T, Dan T, Yang S, Li Y, Luo B, Zhuang Y, Fan X, Zhang X, Cai H, Teng Y. AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images. PATTERNS (NEW YORK, N.Y.) 2023; 4:100806. [PMID: 37720337 PMCID: PMC10499858 DOI: 10.1016/j.patter.2023.100806] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/02/2023] [Accepted: 07/07/2023] [Indexed: 09/19/2023]
Abstract
Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.
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Affiliation(s)
- Ruicun Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Tuoyu Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Tingting Dan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510600, China
| | - Shan Yang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Yanbing Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Boyu Luo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Yingtan Zhuang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Xinyue Fan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Xianchao Zhang
- Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province, Jiaxing University, Jiaxing 314001, China
- Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, Jiaxing 314001, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510600, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
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11
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Madhu G, Mohamed AW, Kautish S, Shah MA, Ali I. Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks. Sci Rep 2023; 13:13377. [PMID: 37591916 PMCID: PMC10435521 DOI: 10.1038/s41598-023-40317-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023] Open
Abstract
Malaria is an acute fever sickness caused by the Plasmodium parasite and spread by infected Anopheles female mosquitoes. It causes catastrophic illness if left untreated for an extended period, and delaying exact treatment might result in the development of further complications. The most prevalent method now available for detecting malaria is the microscope. Under a microscope, blood smears are typically examined for malaria diagnosis. Despite its advantages, this method is time-consuming, subjective, and requires highly skilled personnel. Therefore, an automated malaria diagnosis system is imperative for ensuring accurate and efficient treatment. This research develops an innovative approach utilizing an urgent, inception-based capsule network to distinguish parasitized and uninfected cells from microscopic images. This diagnostic model incorporates neural networks based on Inception and Imperative Capsule networks. The inception block extracts rich characteristics from images of malaria cells using a pre-trained model, such as Inception V3, which facilitates efficient representation learning. Subsequently, the dynamic imperative capsule neural network detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The experiment results demonstrate a significant improvement in malaria parasite recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient diagnostic solutions by leveraging state-of-the-art technologies to combat malaria.
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Affiliation(s)
- Golla Madhu
- Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India
| | - Ali Wagdy Mohamed
- Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
- Applied Science Research Center, Applied Science Private University, Amman, Jordan
| | - Sandeep Kautish
- LBEF Campus (Asia Pacific University of Technology & Innovation, Malaysia), Kathmandu, 44600, Nepal
| | - Mohd Asif Shah
- College of Business and Economics, Kabridahar University, Po Box 250, Kabridahar, Ethiopia.
- School of Business, Woxsen University, Kamkole, Sadasivpet, Hyderabad, 502345, Telangana, India.
- Division of Research and Development, Lovely Professional University, Phagwara, 144001, Punjab, India.
| | - Irfan Ali
- Department of Statistics & Operations Research, Aligarh Muslim University, Aligarh, 202002, India
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12
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Danardono GB, Handayani N, Louis CM, Polim AA, Sirait B, Periastiningrum G, Afadlal S, Boediono A, Sini I. Embryo ploidy status classification through computer-assisted morphology assessment. AJOG GLOBAL REPORTS 2023; 3:100209. [PMID: 37645653 PMCID: PMC10461251 DOI: 10.1016/j.xagr.2023.100209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Preimplantation genetic testing for aneuploidy has been proven to be effective in determining the embryo's chromosomal or ploidy status. The test requires a biopsy of embryonic cells on day 3, 5, or 6 from which complete information on the chromosomes would be obtained. The main drawbacks of preimplantation genetic testing for aneuploidy include its relatively invasive approach and the lack of research studies on the long-term effects of preimplantation genetic testing for aneuploidy. OBJECTIVE Computer-assisted predictive modeling through machine learning and deep learning algorithms has been proposed to minimize the use of invasive preimplantation genetic testing for aneuploidy. The capability to predict morphologic characteristics of embryo ploidy status creates a meaningful support system for decision-making before further treatment. STUDY DESIGN Image processing is a component in developing a predictive model specialized in image classification through which a model is able to differentiate images based on unique features. Image processing is obtained through image augmentation to capture segmented embryos and perform feature extraction. Furthermore, multiple machine learning and deep learning algorithms were used to create prediction-based modeling, and all of the prediction models undergo similar model performance assessments to determine the best model prediction algorithm. RESULTS An efficient artificial intelligence model that can predict embryo ploidy status was developed using image processing through a histogram of oriented gradient and then followed by principal component analysis. The gradient boosting algorithm showed an advantage against other algorithms and yielded an accuracy of 0.74, an aneuploid precision of 0.83, and an aneuploid predictive value (recall) of 0.84. CONCLUSION This research study proved that machine-assisted technology perceives the embryo differently than human observation and determined that further research on in vitro fertilization is needed. The study finding serves as a basis for developing a better computer-assisted prediction model.
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Affiliation(s)
- Gunawan Bondan Danardono
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Nining Handayani
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Claudio Michael Louis
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Arie Adrianus Polim
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia (Dr Polim)
| | - Batara Sirait
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Faculty of Medicine, Department of Obstetrics and Gynaecology, Universitas Kristen Indonesia, Jakarta, Indonesia (Dr Sirait)
| | - Gusti Periastiningrum
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Szeifoul Afadlal
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
| | - Arief Boediono
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Department of Anatomy, Physiology, and Pharmacology, Bogor Agricultural Institute University, Bogor, Indonesia (Dr Boediono)
| | - Ivan Sini
- IRSI Research and Training Centre, Jakarta, Indonesia (Mr Danardono, Ms Handayani, Mr Louis, Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
- Morula IVF Jakarta Clinic, Jakarta, Indonesia (Drs Polim and Sirait, Ms Periastiningrum, and Mr Afadlal, Drs Boediono, and Sini)
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13
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Alonso A, Kirkegaard JB. Fast detection of slender bodies in high density microscopy data. Commun Biol 2023; 6:754. [PMID: 37468539 PMCID: PMC10356847 DOI: 10.1038/s42003-023-05098-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/05/2023] [Indexed: 07/21/2023] Open
Abstract
Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.
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Affiliation(s)
- Albert Alonso
- Niels Bohr Institute & Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Julius B Kirkegaard
- Niels Bohr Institute & Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
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14
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Shewajo FA, Fante KA. Tile-based microscopic image processing for malaria screening using a deep learning approach. BMC Med Imaging 2023; 23:39. [PMID: 36949382 PMCID: PMC10035268 DOI: 10.1186/s12880-023-00993-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 03/08/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Manual microscopic examination remains the golden standard for malaria diagnosis. But it is laborious, and pathologists with experience are needed for accurate diagnosis. The need for computer-aided diagnosis methods is driven by the enormous workload and difficulties associated with manual microscopy based examination. While the importance of computer-aided diagnosis is increasing at an enormous pace, fostered by the advancement of deep learning algorithms, there are still challenges in detecting small objects such as malaria parasites in microscopic images of blood films. The state-of-the-art (SOTA) deep learning-based object detection models are inefficient in detecting small objects accurately because they are underrepresented on benchmark datasets. The performance of these models is affected by the loss of detailed spatial information due to in-network feature map downscaling. This is due to the fact that the SOTA models cannot directly process high-resolution images due to their low-resolution network input layer. METHODS In this study, an efficient and robust tile-based image processing method is proposed to enhance the performance of malaria parasites detection SOTA models. Three variants of YOLOV4-based object detectors are adopted considering their detection accuracy and speed. These models were trained using tiles generated from 1780 high-resolution P. falciparum-infected thick smear microscopic images. The tiling of high-resolution images improves the performance of the object detection models. The detection accuracy and the generalization capability of these models have been evaluated using three datasets acquired from different regions. RESULTS The best-performing model using the proposed tile-based approach outperforms the baseline method significantly (Recall, [95.3%] vs [57%] and Average Precision, [87.1%] vs [76%]). Furthermore, the proposed method has outperformed the existing approaches that used different machine learning techniques evaluated on similar datasets. CONCLUSIONS The experimental results show that the proposed method significantly improves P. falciparum detection from thick smear microscopic images while maintaining real-time detection speed. Furthermore, the proposed method has the potential to assist and reduce the workload of laboratory technicians in malaria-endemic remote areas of developing countries where there is a critical skill gap and a shortage of experts.
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Affiliation(s)
| | - Kinde Anlay Fante
- Faculty of Electrical and Computer Engineering, Jimma University, 378, Jimma, Ethiopia
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15
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de Korne CM, van Lieshout L, van Leeuwen FWB, Roestenberg M. Imaging as a (pre)clinical tool in parasitology. Trends Parasitol 2023; 39:212-226. [PMID: 36641293 DOI: 10.1016/j.pt.2022.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/23/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023]
Abstract
Imaging of parasites is central to diagnosis of many parasitic diseases and has thus far played an important role in the development of antiparasitic strategies. The development of novel imaging technologies has revolutionized medicine in fields other than parasitology and has also opened up new avenues for the visualization of parasites. Here we review the role imaging technology has played so far in parasitology and how it may spur further advancement. We point out possibilities to improve current microscopy-based diagnostic methods and how to extend them with radiological imaging modalities. We also highlight in vivo tracking of parasites as a readout for efficacy of new antiparasitic strategies and as a source of fundamental insights for rational design.
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Affiliation(s)
- Clarize Maria de Korne
- Leiden University Center for Infectious Diseases, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands; Interventional Molecular Imaging laboratory, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Lisette van Lieshout
- Leiden University Center for Infectious Diseases, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Fijs Willem Bernhard van Leeuwen
- Interventional Molecular Imaging laboratory, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Meta Roestenberg
- Leiden University Center for Infectious Diseases, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands.
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16
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Ohta Y, Tateishi E, Morita Y, Nishii T, Kotoku A, Horinouchi H, Fukuyama M, Fukuda T. Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone. Eur Radiol 2023:10.1007/s00330-023-09465-8. [PMID: 36809433 DOI: 10.1007/s00330-023-09465-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 09/27/2022] [Accepted: 01/22/2023] [Indexed: 02/23/2023]
Abstract
OBJECTIVES To determine the optimal inversion time (TI) from Look-Locker scout images using a convolutional neural network (CNN) and to investigate the feasibility of correcting TI using a smartphone. METHODS In this retrospective study, TI-scout images were extracted using a Look-Locker approach from 1113 consecutive cardiac MR examinations performed between 2017 and 2020 with myocardial late gadolinium enhancement. Reference TI null points were independently determined visually by an experienced radiologist and an experienced cardiologist, and quantitatively measured. A CNN was developed to evaluate deviation of TI from the null point and then implemented in PC and smartphone applications. Images on 4 K or 3-megapixel monitors were captured by a smartphone, and CNN performance on each monitor was determined. Optimal, undercorrection, and overcorrection rates using deep learning on the PC and smartphone were calculated. For patient analysis, TI category differences in pre- and post-correction were evaluated using the TI null point used in late gadolinium enhancement imaging. RESULTS For PC, 96.4% (772/749) of images were classified as optimal, with under- and overcorrection rates of 1.2% (9/749) and 2.4% (18/749), respectively. For 4 K images, 93.5% (700/749) of images were classified as optimal, with under- and overcorrection rates of 3.9% (29/749) and 2.7% (20/749), respectively. For 3-megapixel images, 89.6% (671/749) of images were classified as optimal, with under- and overcorrection rates of 3.3% (25/749) and 7.0% (53/749), respectively. On patient-based evaluations, subjects classified as within optimal range increased from 72.0% (77/107) to 91.6% (98/107) using the CNN. CONCLUSIONS Optimizing TI on Look-Locker images was feasible using deep learning and a smartphone. KEY POINTS • A deep learning model corrected TI-scout images to within optimal null point for LGE imaging. • By capturing the TI-scout image on the monitor with a smartphone, the deviation of the TI from the null point can be immediately determined. • Using this model, TI null points can be set to the same degree as that by an experienced radiological technologist.
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Affiliation(s)
- Yasutoshi Ohta
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan.
| | - Emi Tateishi
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
| | - Yoshiaki Morita
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
| | - Tatsuya Nishii
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
| | - Akiyuki Kotoku
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
| | - Hiroki Horinouchi
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
| | - Midori Fukuyama
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
| | - Tetsuya Fukuda
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan
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17
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Palekar S, Kalambe J, Patrikar RM. IoT enabled microfluidics-based biochemistry analyzer based on colorimetric detection techniques. CHEMICAL PAPERS 2023. [DOI: 10.1007/s11696-023-02678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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18
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Aris TA, Nasir ASA, Mustafa WA, Mashor MY, Haryanto EV, Mohamed Z. Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images. Diagnostics (Basel) 2023; 13:diagnostics13030511. [PMID: 36766620 PMCID: PMC9913904 DOI: 10.3390/diagnostics13030511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/16/2023] [Accepted: 01/26/2023] [Indexed: 02/02/2023] Open
Abstract
Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites' stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.
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Affiliation(s)
- Thaqifah Ahmad Aris
- Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Malaysia
| | - Aimi Salihah Abdul Nasir
- Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Malaysia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Malaysia
- Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Malaysia
- Correspondence:
| | - Mohd Yusoff Mashor
- Faculty of Electronic Engineering and Technology, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Malaysia
| | - Edy Victor Haryanto
- Faculty of Engineering and Computer Science, Universitas Potensi Utama, Medan 20241, Indonesia
| | - Zeehaida Mohamed
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
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19
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Yu H, Mohammed FO, Abdel Hamid M, Yang F, Kassim YM, Mohamed AO, Maude RJ, Ding XC, Owusu ED, Yerlikaya S, Dittrich S, Jaeger S. Patient-level performance evaluation of a smartphone-based malaria diagnostic application. Malar J 2023; 22:33. [PMID: 36707822 PMCID: PMC9883923 DOI: 10.1186/s12936-023-04446-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 01/06/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis. METHODS A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net. RESULTS Malaria Screener reached 74.1% (95% CI 63.5-83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0-81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8-96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0-88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6-86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development. CONCLUSION Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.
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Affiliation(s)
- Hang Yu
- grid.280285.50000 0004 0507 7840Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, MD Bethesda, USA
| | - Fayad O. Mohammed
- grid.9763.b0000 0001 0674 6207Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, Medical Campus, University of Khartoum, Khartoum, Sudan
| | - Muzamil Abdel Hamid
- grid.9763.b0000 0001 0674 6207Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, Medical Campus, University of Khartoum, Khartoum, Sudan
| | - Feng Yang
- grid.280285.50000 0004 0507 7840Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, MD Bethesda, USA
| | - Yasmin M. Kassim
- grid.280285.50000 0004 0507 7840Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, MD Bethesda, USA
| | - Abdelrahim O. Mohamed
- grid.9763.b0000 0001 0674 6207Department of Parasitology and Medical Entomology, Institute of Endemic Diseases, Medical Campus, University of Khartoum, Khartoum, Sudan ,grid.9763.b0000 0001 0674 6207Department of Biochemistry, Faculty of Medicine, University of Khartoum, Khartoum, Sudan
| | - Richard J. Maude
- grid.10223.320000 0004 1937 0490Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.38142.3c000000041936754XHarvard TH Chan School of Public Health, Harvard University, Boston, USA
| | - Xavier C. Ding
- grid.452485.a0000 0001 1507 3147FIND, Geneva, Switzerland
| | - Ewurama D.A. Owusu
- grid.452485.a0000 0001 1507 3147FIND, Geneva, Switzerland ,grid.8652.90000 0004 1937 1485Department of Medical Laboratory Sciences, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra, Ghana
| | - Seda Yerlikaya
- grid.452485.a0000 0001 1507 3147FIND, Geneva, Switzerland
| | | | - Stefan Jaeger
- grid.280285.50000 0004 0507 7840Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, MD Bethesda, USA
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20
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Aqeel S, Haider Z, Khan W. Towards digital diagnosis of malaria: How far have we reached? J Microbiol Methods 2023; 204:106630. [PMID: 36503827 DOI: 10.1016/j.mimet.2022.106630] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/24/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022]
Abstract
The need for precise and early diagnosis of malaria and its distinction from other febrile illnesses is no doubt a prerequisite, primarily when standard rapid diagnostic tests (RDTs) cannot be totally relied upon. At the time of disease outbreaks, the pressure on hospital staff remains high and the chances of human error increase. Therefore, in the era of digitalisation of medicine as well as diagnostic approaches, various technologies such as artificial intelligence (AI) and machine learning (ML) should be deployed to further aid the diagnosis, especially in endemic and epidemic situations. Computational techniques are now more at the forefront than ever, and the interest in developing such efficient technologies is continuously increasing. A comprehensive understanding of these digital technologies is needed to maintain the scientific rigour in these attempts. This would enhance the implementation of these novel technologies for malaria diagnosis. This review highlights the progression, strengths, and limitations of various computing techniques so far employed to diagnose malaria.
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Affiliation(s)
- Sana Aqeel
- Department of Zoology, Aligarh Muslim University, Aligarh, India.
| | - Zafaryab Haider
- Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, India
| | - Wajihullah Khan
- Department of Zoology, Aligarh Muslim University, Aligarh, India
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21
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Deelder W, Manko E, Phelan JE, Campino S, Palla L, Clark TG. Geographical classification of malaria parasites through applying machine learning to whole genome sequence data. Sci Rep 2022; 12:21150. [PMID: 36476815 PMCID: PMC9729610 DOI: 10.1038/s41598-022-25568-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.
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Affiliation(s)
- Wouter Deelder
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Dalberg Advisors, 7 Rue de Chantepoulet, 1201, Geneva, Switzerland
| | - Emilia Manko
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Jody E Phelan
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Susana Campino
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Luigi Palla
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Department of Public Health and Infectious Diseases, University of Rome La Sapienza, Rome, Italy
| | - Taane G Clark
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
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22
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Ikerionwu C, Ugwuishiwu C, Okpala I, James I, Okoronkwo M, Nnadi C, Orji U, Ebem D, Ike A. Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future. Photodiagnosis Photodyn Ther 2022; 40:103198. [PMID: 36379305 DOI: 10.1016/j.pdpdt.2022.103198] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/14/2022]
Abstract
Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. According to the World Health Organisation (WHO), millions of people are infected annually, leading to inevitable deaths in the infected population. Statistical records show that early detection of malaria parasites could prevent deaths and machine learning (ML) has proved helpful in the early detection of malarial parasites. Human error is identified to be a major cause of inaccurate diagnostics in the traditional microscopy malaria diagnosis method. Therefore, the method would be more reliable if human expert dependency is restricted or entirely removed, and thus, the motivation of this paper. This study presents a systematic review to understand the prevalent machine learning algorithms applied to a low-cost, portable optical microscope in the automation of blood film interpretation for malaria parasite detection. Peer-reviewed papers were downloaded from selected reputable databases eg. Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, etc. The extant literature suggests that convolutional neural network (CNN) and its variants (deep learning) account for 41.9% of the microscopy malaria diagnosis using machine learning with a prediction accuracy of 99.23%. Thus, the findings suggest that early detection of the malaria parasite has improved through the application of CNN and other ML algorithms on microscopic malaria parasite detection.
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Affiliation(s)
- Charles Ikerionwu
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Software Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
| | - Chikodili Ugwuishiwu
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Computer Science, University of Nigeria, Nsukka, Enugu State, Nigeria.
| | - Izunna Okpala
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Information Technology, University of Cincinnati, USA
| | - Idara James
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Computer Science, Akwa Ibom State University, Nigeria
| | - Matthew Okoronkwo
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Computer Science, University of Nigeria, Nsukka, Enugu State, Nigeria
| | - Charles Nnadi
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Deprtment of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, Enugu State, Nigeria
| | - Ugochukwu Orji
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Computer Science, University of Nigeria, Nsukka, Enugu State, Nigeria
| | - Deborah Ebem
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Computer Science, University of Nigeria, Nsukka, Enugu State, Nigeria
| | - Anthony Ike
- Machine Learning on Disease Diagnosis Research Group, Nigeria; Department of Microbiology, University of Nigeria, Nsukka, Enugu State, Nigeria
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23
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A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases. Trop Med Infect Dis 2022; 7:tropicalmed7120398. [PMID: 36548653 PMCID: PMC9787706 DOI: 10.3390/tropicalmed7120398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/17/2022] [Accepted: 11/21/2022] [Indexed: 11/29/2022] Open
Abstract
This systematic literature aims to identify soft computing techniques currently utilized in diagnosing tropical febrile diseases and explore the data characteristics and features used for diagnoses, algorithm accuracy, and the limitations of current studies. The goal of this study is therefore centralized around determining the extent to which soft computing techniques have positively impacted the quality of physician care and their effectiveness in tropical disease diagnosis. The study has used PRISMA guidelines to identify paper selection and inclusion/exclusion criteria. It was determined that the highest frequency of articles utilized ensemble techniques for classification, prediction, analysis, diagnosis, etc., over single machine learning techniques, followed by neural networks. The results identified dengue fever as the most studied disease, followed by malaria and tuberculosis. It was also revealed that accuracy was the most common metric utilized to evaluate the predictive capability of a classification mode. The information presented within these studies benefits frontline healthcare workers who could depend on soft computing techniques for accurate diagnoses of tropical diseases. Although our research shows an increasing interest in using machine learning techniques for diagnosing tropical diseases, there still needs to be more studies. Hence, recommendations and directions for future research are proposed.
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24
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Maturana CR, de Oliveira AD, Nadal S, Bilalli B, Serrat FZ, Soley ME, Igual ES, Bosch M, Lluch AV, Abelló A, López-Codina D, Suñé TP, Clols ES, Joseph-Munné J. Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review. Front Microbiol 2022; 13:1006659. [PMID: 36458185 PMCID: PMC9705958 DOI: 10.3389/fmicb.2022.1006659] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 09/26/2022] [Indexed: 09/03/2023] Open
Abstract
Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.
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Affiliation(s)
- Carles Rubio Maturana
- Microbiology Department, Vall d’Hebron Research Institute, Vall d’Hebron Hospital Campus, Barcelona, Spain
- Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Allisson Dantas de Oliveira
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
| | - Sergi Nadal
- Data Base Technologies and Information Group, Engineering Services and Information Systems Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Besim Bilalli
- Data Base Technologies and Information Group, Engineering Services and Information Systems Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Francesc Zarzuela Serrat
- Microbiology Department, Vall d’Hebron Research Institute, Vall d’Hebron Hospital Campus, Barcelona, Spain
| | - Mateu Espasa Soley
- Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
- Clinical Laboratories, Microbiology Department, Hospital Universitari Parc Taulí, Sabadell, Spain
| | - Elena Sulleiro Igual
- Microbiology Department, Vall d’Hebron Research Institute, Vall d’Hebron Hospital Campus, Barcelona, Spain
- Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
- CIBERINFEC, ISCIII- CIBER de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain
| | | | | | - Alberto Abelló
- Data Base Technologies and Information Group, Engineering Services and Information Systems Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Daniel López-Codina
- Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain
| | - Tomàs Pumarola Suñé
- Microbiology Department, Vall d’Hebron Research Institute, Vall d’Hebron Hospital Campus, Barcelona, Spain
- Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Elisa Sayrol Clols
- Image Processing Group, Telecommunications and Signal Theory Group, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Joan Joseph-Munné
- Microbiology Department, Vall d’Hebron Research Institute, Vall d’Hebron Hospital Campus, Barcelona, Spain
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25
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Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears. Diagnostics (Basel) 2022; 12:diagnostics12112702. [DOI: 10.3390/diagnostics12112702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.
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26
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Bhuiyan M, Islam MS. A new ensemble learning approach to detect malaria from microscopic red blood cell images. SENSORS INTERNATIONAL 2022. [DOI: 10.1016/j.sintl.2022.100209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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27
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Ashraf S, Khalid A, de Vos AL, Feng Y, Rohrbach P, Hasan T. Malaria Detection Accelerated: Combing a High-Throughput NanoZoomer Platform with a ParasiteMacro Algorithm. Pathogens 2022; 11:pathogens11101182. [PMID: 36297240 PMCID: PMC9606851 DOI: 10.3390/pathogens11101182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/05/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
Eradication of malaria, a mosquito-borne parasitic disease that hijacks human red blood cells, is a global priority. Microscopy remains the gold standard hallmark for diagnosis and estimation of parasitemia for malaria, to date. However, this approach is time-consuming and requires much expertise especially in malaria-endemic countries or in areas with low-density malaria infection. Thus, there is a need for accurate malaria diagnosis/parasitemia estimation with standardized, fast, and more reliable methods. To this end, we performed a proof-of-concept study using the automated imaging (NanoZoomer) platform to detect the malarial parasite in infected blood. The approach can be used as a steppingstone for malaria diagnosis and parasitemia estimation. Additionally, we created an algorithm (ParasiteMacro) compatible with free online imaging software (ImageJ) that can be used with low magnification objectives (e.g., 5×, 10×, and 20×) both in the NanoZoomer and routine microscope. The novel approach to estimate malarial parasitemia based on modern technologies compared to manual light microscopy demonstrated 100% sensitivity, 87% specificity, a 100% negative predictive value (NPV) and a 93% positive predictive value (PPV). The manual and automated malaria counts showed a good Pearson correlation for low- (R2 = 0.9377, r = 0.9683 and p < 0.0001) as well as high- parasitemia (R2 = 0.8170, r = 0.9044 and p < 0.0001) with low estimation errors. Our robust strategy that identifies and quantifies malaria can play a pivotal role in disease control strategies.
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Affiliation(s)
- Shoaib Ashraf
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Boston, MA 02114, USA
- Department of Animal Science, McGill University, Sainte-Anne-de-Bellevue, QC H9X3V9, Canada
| | - Areeba Khalid
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Boston, MA 02114, USA
- Department of Computer Science, Mathematics Adelphi University, Garden City, NY 11530, USA
- Department of Biomedical Engineering, Tufts University, Medford, OR 02155, USA
| | - Arend L. de Vos
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Boston, MA 02114, USA
- Swammerdam Institute of Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Yanfang Feng
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Boston, MA 02114, USA
| | - Petra Rohrbach
- Institute of Parasitology, McGill University, Sainte-Anne-de-Bellevue, QC H9X3V9, Canada
| | - Tayyaba Hasan
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, 40 Blossom Street, Boston, MA 02114, USA
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Correspondence:
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28
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Preißinger K, Kellermayer M, Vértessy BG, Kézsmárki I, Török J. Reducing data dimension boosts neural network-based stage-specific malaria detection. Sci Rep 2022; 12:16389. [PMID: 36180456 PMCID: PMC9523653 DOI: 10.1038/s41598-022-19601-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
Although malaria has been known for more than 4 thousand years1, it still imposes a global burden with approx. 240 million annual cases2. Improvement in diagnostic techniques is a prerequisite for its global elimination. Despite its main limitations, being time-consuming and subjective, light microscopy on Giemsa-stained blood smears is still the gold-standard diagnostic method used worldwide. Autonomous computer assisted recognition of malaria infected red blood cells (RBCs) using neural networks (NNs) has the potential to overcome these deficiencies, if a fast, high-accuracy detection can be achieved using low computational power and limited sets of microscopy images for training the NN. Here, we report on a novel NN-based scheme that is capable of the high-speed classification of RBCs into four categories—healthy ones and three classes of infected ones according to the parasite age—with an accuracy as high as 98%. Importantly, we observe that a smart reduction of data dimension, using characteristic one-dimensional cross-sections of the RBC images, not only speeds up the classification but also significantly improves its performance with respect to the usual two-dimensional NN schemes. Via comparative studies on RBC images recorded by two additional techniques, fluorescence and atomic force microscopy, we demonstrate that our method is universally applicable for different types of microscopy images. This robustness against imaging platform-specific features is crucial for diagnostic applications. Our approach for the reduction of data dimension could be straightforwardly generalised for the classification of different parasites, cells and other types of objects.
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Affiliation(s)
- Katharina Preißinger
- Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, 1111, Hungary. .,Institute of Enzymology, Research Center for Natural Sciences, Budapest, 1111, Hungary. .,Department of Physics, Budapest University of Technology and Economics, Budapest, 1111, Hungary. .,Department of Experimental Physics V, University of Augsburg, 86159, Augsburg, Germany.
| | - Miklós Kellermayer
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, 1111, Hungary
| | - Beáta G Vértessy
- Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, 1111, Hungary.,Institute of Enzymology, Research Center for Natural Sciences, Budapest, 1111, Hungary
| | - István Kézsmárki
- Department of Physics, Budapest University of Technology and Economics, Budapest, 1111, Hungary.,Department of Experimental Physics V, University of Augsburg, 86159, Augsburg, Germany
| | - János Török
- Department of Theoretical Physics, Institute of Physics, Budapest University of Technology and Economics, Budapest, 1111, Hungary.,MTA-BME Morphodynamics Research Group, Budapest University of Technology and Economics, Budapest, 1111, Hungary
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29
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Zio S, Lamien B, Tiemounou S, Adaman Y, Tougri I, Beidari M, Boris OWYS. Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters. Infect Dis Model 2022; 7:448-462. [PMID: 35845472 PMCID: PMC9270784 DOI: 10.1016/j.idm.2022.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/14/2022] [Accepted: 06/24/2022] [Indexed: 12/23/2022] Open
Abstract
The novel coronavirus has affected all regions of the world, but each country has experienced different rates of infection. In West Africa, in particular, infection rates remain low as compared to other parts of the world. This heterogeneity in the spread of COVID-19 raises a lot of questions that are still unanswered. However, some studies point out that people's mobility, size of gatherings, rate of testing, and weather have a great impact on the COVID-19 spread. In this work, we first evaluate the correlation between meteorological parameters and COVID-19 cases using Spearman's rank correlation. Secondly, multi-output Gaussian processes (MOGP) are used to predict the daily confirmed COVID-19 cases by exploring its relationships with meteorological parameters. The number of daily reported COVID-19 cases, as well as, weather variables collected from March 9, 2020, to October 18, 2021, were used in the analysis. The weather variables considered in the analysis are the mean temperature, relative humidity, wind direction, insolation, precipitation, and wind speed. The predicting model was constructed exploiting the correlation between the data of the daily confirmed COVID-19 cases and data of the weather variables. The results show that a significant correlation between the daily confirmed COVID-19 cases was found with humidity, wind direction, wind speed, and insolation. These parameters are used to construct the predictive model using the Multi-Output Gaussian process (MOGP). Different combinations of the data of meteorological parameters together with the data of daily reported COVID-19 cases were used to derive different models. We found that the best predictor is obtained using the combination of Humidity and insolation. This model is then used to predict the daily confirmed COVID-19 cases knowing the humidity and Insolation.
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Affiliation(s)
- Souleymane Zio
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Bernard Lamien
- Institut du Génie Industriel et Textile, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Sibiri Tiemounou
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Yoda Adaman
- Agence Nationale de la Météorologie du Burkina Faso, ANAM, Burkina Faso
| | - Inoussa Tougri
- Institut du Génie Industriel et Textile, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Mohamed Beidari
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
| | - Ouedraogo W Y S Boris
- Institut du Génie Informatique et Telecom, École Polytechnique de Ouagadougou, Ouaga, 2000, Burkina Faso
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30
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Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach. BMC Bioinformatics 2022; 23:325. [PMID: 35934714 PMCID: PMC9358850 DOI: 10.1186/s12859-022-04870-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 08/01/2022] [Indexed: 11/25/2022] Open
Abstract
Background The malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model. More specifically, this study aims to quantify an individual's susceptibility to the development of malaria by using risk scores obtained from the cumulative effects of SNPs, known as weighted genetic risk scores (wGRS).
Results We proposed an SNP-based feature extraction algorithm that incorporates the susceptibility information of an individual to malaria to generate the feature set. However, it can become computationally expensive for a machine learning model to learn from many SNPs. Therefore, we reduced the feature set by employing the Logistic Regression and Recursive Feature Elimination (LR-RFE) method to select SNPs that improve the efficacy of our model. Next, we calculated the wGRS of the selected feature set, which is used as the model's target variables. Moreover, to compare the performance of the wGRS-only model, we calculated and evaluated the combination of wGRS with genotype frequency (wGRS + GF). Finally, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Ridge regression algorithms are utilized to establish the machine learning models for malaria risk prediction. Conclusions Our proposed approach identified SNP rs334 as the most contributing feature with an importance score of 6.224 compared to the baseline, with an importance score of 1.1314. This is an important result as prior studies have proven that rs334 is a major genetic risk factor for malaria. The analysis and comparison of the three machine learning models demonstrated that LightGBM achieves the highest model performance with a Mean Absolute Error (MAE) score of 0.0373. Furthermore, based on wGRS + GF, all models performed significantly better than wGRS alone, in which LightGBM obtained the best performance (0.0033 MAE score). Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04870-0.
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van Wyk CL, Mtshali K, Taioe MO, Terera S, Bakkes D, Ramatla T, Xuan X, Thekisoe O. Detection of Ticks and Tick-Borne Pathogens of Urban Stray Dogs in South Africa. Pathogens 2022; 11:pathogens11080862. [PMID: 36014983 PMCID: PMC9416273 DOI: 10.3390/pathogens11080862] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/14/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022] Open
Abstract
This study aimed to identify ticks infesting dogs admitted to the Potchefstroom Animal Welfare Society (PAWS) and to detect tick-borne pathogens they are harbouring. A total of 592 ticks were collected from 61 stray dogs admitted to PAWS originating from several suburbs in and near Potchefstroom, South Africa. The dog ticks were identified as Haemaphysalis elliptica (39%) and Rhipicephalus sanguineus (61%) by both morphological and DNA analyses. Of these ticks, H. elliptica consisted of 67.5% (156/231) and 32.5% (75/231) female and male ticks, respectively, whilst R. sanguineus consisted of 48.5% (175/361) and 51.5% (186/361) female and male ticks, respectively. Microscopic examination of blood smears from engorged female ticks indicated overall occurrences of 0.5% (1/204) for Babesia spp. from R. sanguineus, 1% (2/204) of Anaplasma spp. from H. elliptica, and 22% (45/204) of Rickettsia spp. from both H. elliptica and R. sanguineus. Using pooled samples molecular detection of tick-borne pathogens indicated overall occurrences of 1% (1/104) for A. phagocytophilum in H. elliptica, 9.6% (10/104) of Rickettsia spp. in H. elliptica and R. sanguineus, 5.8% (6/104) of Ehrlichia canis in H. elliptica and R. sanguineus, and 13.5% (14/104) of Coxiella spp. in both H. elliptica and R. sanguineus. Additionally, PCR detected 6.5% (2/31) of Coxiella spp. DNA from H. elliptica eggs. Our data indicate that urban stray dogs admitted at PAWS are infested by H. elliptica and R. sanguineus ticks which are harbouring several pathogenic organisms known to cause tick-borne diseases.
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Affiliation(s)
- Clara-Lee van Wyk
- Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa; (C.-L.v.W.); (M.O.T.); (T.R.); (O.T.)
| | - Khethiwe Mtshali
- Department of Biomedical Sciences, Tshwane University of Technology, Arcadia Campus, Pretoria 0001, South Africa;
| | - Moeti O. Taioe
- Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa; (C.-L.v.W.); (M.O.T.); (T.R.); (O.T.)
- Epidemiology, Parasites and Vectors, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort 0110, South Africa
| | - Stallone Terera
- Potchefstroom Animal Welfare Society, Potchefstroom 2531, South Africa;
| | - Deon Bakkes
- Gertrud Theiler Tick Museum, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort 0110, South Africa;
| | - Tsepo Ramatla
- Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa; (C.-L.v.W.); (M.O.T.); (T.R.); (O.T.)
| | - Xuenan Xuan
- National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro 080-8555, Japan
- Correspondence:
| | - Oriel Thekisoe
- Unit for Environmental Sciences and Management, North-West University, Potchefstroom 2520, South Africa; (C.-L.v.W.); (M.O.T.); (T.R.); (O.T.)
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Lubell-Doughtie P, Bhatt S, Wong R, Shankar AH. Transforming Rapid Diagnostic Tests for Precision Public Health: Open Guidelines for Manufacturers and Users. JMIR BIOMEDICAL ENGINEERING 2022; 7:e26800. [PMID: 38875688 PMCID: PMC11041428 DOI: 10.2196/26800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 07/24/2021] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis. OBJECTIVE We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI); identifying design features to maximize the IUI; and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system. METHODS We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization-prequalified RDTs. RDT-OG specifications were developed to maximize the IUI; the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia. RESULTS The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype; (2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources; and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems. CONCLUSIONS Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.
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Affiliation(s)
| | | | - Roger Wong
- Ona Systems Inc, Burlington, VT, United States
| | - Anuraj H Shankar
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
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Computational Models-Based Detection of Peripheral Malarial Parasites in Blood Smears. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:9171343. [PMID: 35800239 PMCID: PMC9200540 DOI: 10.1155/2022/9171343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 11/26/2022]
Abstract
The most common human parasite as per the medical experts is the malarial disease, which is caused by a protozoan parasite, and Plasmodium falciparum, a common parasite in humans. A microscopist with expertise in malaria diagnosis must conduct this complex procedure to identify the stages of infection. This epidemic is an ongoing disease in some parts of the world, which is commonly found. A Kaggle repository was used to upload the data collected from the NIH portal. The dataset contains 27558 samples, of which 13779 samples carry parasites and 13779 samples do not. This paper focuses on two of the most common deep transfer learning methods. Unlike other feature extractors, VGG-19's fine-tuning and pretraining made it an ideal feature extractor. Several image classification models, including VGG-19, have been pretrained on larger datasets. Additionally, deep learning strategies based on pretrained models are proposed for detecting malarial parasite cases in the early stages, in addition to an accuracy rating of 98.34∗ 0.51%.
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Islam MR, Nahiduzzaman M, Goni MOF, Sayeed A, Anower MS, Ahsan M, Haider J. Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images. SENSORS 2022; 22:s22124358. [PMID: 35746136 PMCID: PMC9230392 DOI: 10.3390/s22124358] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/03/2022] [Accepted: 06/05/2022] [Indexed: 02/06/2023]
Abstract
Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.
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Affiliation(s)
- Md. Robiul Islam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh; (M.N.); (M.O.F.G.)
- Correspondence: (M.R.I.); (M.A.)
| | - Md. Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh; (M.N.); (M.O.F.G.)
| | - Md. Omaer Faruq Goni
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh; (M.N.); (M.O.F.G.)
| | - Abu Sayeed
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh;
| | - Md. Shamim Anower
- Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh;
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK
- Correspondence: (M.R.I.); (M.A.)
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UK;
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An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images. Comput Biol Med 2022; 148:105635. [DOI: 10.1016/j.compbiomed.2022.105635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 04/15/2022] [Accepted: 04/28/2022] [Indexed: 11/18/2022]
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Dutta AK, Mageswari RU, Gayathri A, Dallfin Bruxella JM, Ishak MK, Mostafa SM, Hamam H. Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7776319. [PMID: 35694571 PMCID: PMC9177294 DOI: 10.1155/2022/7776319] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/29/2022] [Accepted: 05/07/2022] [Indexed: 01/06/2023]
Abstract
Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.
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Affiliation(s)
- Ashit Kumar Dutta
- Department of Computer Science and Information System, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia
| | - R. Uma Mageswari
- Department of Computer Science and Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, India
| | - A. Gayathri
- Department of Information Technology, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - J. Mary Dallfin Bruxella
- Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India
| | - Mohamad Khairi Ishak
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
| | - Samih M. Mostafa
- Faculty of Computers and Information, South Valley University, Egypt
| | - Habib Hamam
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A 3E9, Canada
- School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
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IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115500] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Deep learning approaches play a crucial role in computer-aided diagnosis systems to support clinical decision-making. However, developing such automated solutions is challenging due to the limited availability of annotated medical data. In this study, we proposed a novel and computationally efficient deep learning approach to leverage small data for learning generalizable and domain invariant representations in different medical imaging applications such as malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets which are capable of generating salient features for a particular problem. Then, we build up ever larger and more powerful networks by combining these SubNets in different configurations. At each stage, only one new SubNet module undergoes learning updates. This reduces the computational resource requirements for training and aids in network optimization. We compare IMNets against classic and state-of-the-art deep learning architectures such as AlexNet, ResNet-50, Inception v3, DenseNet-201, and NasNet for the various experiments conducted in this study. Our proposed IMNS design leads to high average classification accuracies of 97.0%, 97.9%, and 88.6% for malaria, diabetic retinopathy, and tuberculosis, respectively. Our modular design for deep learning achieves the state-of-the-art performance in the scenarios tested. The IMNets produced here have a relatively low computational complexity compared to traditional deep learning architectures. The largest IMNet tested here has 0.95 M of the learnable parameters and 0.08 G of the floating-point multiply–add (MAdd) operations. The simpler IMNets train faster, have lower memory requirements, and process images faster than the benchmark methods tested.
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Alharbi AH, V AC, Lin M, Ashwini B, Jabarulla MY, Shah MA. Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3922763. [PMID: 35655511 PMCID: PMC9155968 DOI: 10.1155/2022/3922763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/17/2022] [Accepted: 04/25/2022] [Indexed: 11/28/2022]
Abstract
Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.
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Affiliation(s)
- Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Aravinda C. V
- N. M. A. M. Institute of Technology, Nitte 574110, Karkala, India
| | - Meng Lin
- Ritsumeikan University, Kyoto, Japan
| | - B Ashwini
- N. M. A. M. Institute of Technology, Nitte 574110, Karkala, India
| | - Mohamed Yaseen Jabarulla
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
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Hu RS, Hesham AEL, Zou Q. Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases. Front Cell Infect Microbiol 2022; 12:882995. [PMID: 35573796 PMCID: PMC9097758 DOI: 10.3389/fcimb.2022.882995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/28/2022] [Indexed: 12/24/2022] Open
Abstract
In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.
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Affiliation(s)
- Rui-Si Hu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, Egypt
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- *Correspondence: Quan Zou,
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Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3626726. [PMID: 35449742 PMCID: PMC9017520 DOI: 10.1155/2022/3626726] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/26/2022] [Indexed: 11/18/2022]
Abstract
Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.
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Das D, Vongpromek R, Assawariyathipat T, Srinamon K, Kennon K, Stepniewska K, Ghose A, Sayeed AA, Faiz MA, Netto RLA, Siqueira A, Yerbanga SR, Ouédraogo JB, Callery JJ, Peto TJ, Tripura R, Koukouikila-Koussounda F, Ntoumi F, Ong’echa JM, Ogutu B, Ghimire P, Marfurt J, Ley B, Seck A, Ndiaye M, Moodley B, Sun LM, Archasuksan L, Proux S, Nsobya SL, Rosenthal PJ, Horning MP, McGuire SK, Mehanian C, Burkot S, Delahunt CB, Bachman C, Price RN, Dondorp AM, Chappuis F, Guérin PJ, Dhorda M. Field evaluation of the diagnostic performance of EasyScan GO: a digital malaria microscopy device based on machine-learning. Malar J 2022; 21:122. [PMID: 35413904 PMCID: PMC9004086 DOI: 10.1186/s12936-022-04146-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Microscopic examination of Giemsa-stained blood films remains the reference standard for malaria parasite detection and quantification, but is undermined by difficulties in ensuring high-quality manual reading and inter-reader reliability. Automated parasite detection and quantification may address this issue. METHODS A multi-centre, observational study was conducted during 2018 and 2019 at 11 sites to assess the performance of the EasyScan Go, a microscopy device employing machine-learning-based image analysis. Sensitivity, specificity, accuracy of species detection and parasite density estimation were assessed with expert microscopy as the reference. Intra- and inter-device reliability of the device was also evaluated by comparing results from repeat reads on the same and two different devices. This study has been reported in accordance with the Standards for Reporting Diagnostic accuracy studies (STARD) checklist. RESULTS In total, 2250 Giemsa-stained blood films were prepared and read independently by expert microscopists and the EasyScan Go device. The diagnostic sensitivity of EasyScan Go was 91.1% (95% CI 88.9-92.7), and specificity 75.6% (95% CI 73.1-78.0). With good quality slides sensitivity was similar (89.1%, 95%CI 86.2-91.5), but specificity increased to 85.1% (95%CI 82.6-87.4). Sensitivity increased with parasitaemia rising from 57% at < 200 parasite/µL, to ≥ 90% at > 200-200,000 parasite/µL. Species were identified accurately in 93% of Plasmodium falciparum samples (kappa = 0.76, 95% CI 0.69-0.83), and in 92% of Plasmodium vivax samples (kappa = 0.73, 95% CI 0.66-0.80). Parasite density estimates by the EasyScan Go were within ± 25% of the microscopic reference counts in 23% of slides. CONCLUSIONS The performance of the EasyScan Go in parasite detection and species identification accuracy fulfil WHO-TDR Research Malaria Microscopy competence level 2 criteria. In terms of parasite quantification and false positive rate, it meets the level 4 WHO-TDR Research Malaria Microscopy criteria. All performance parameters were significantly affected by slide quality. Further software improvement is required to improve sensitivity at low parasitaemia and parasite density estimations. Trial registration ClinicalTrials.gov number NCT03512678.
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Affiliation(s)
- Debashish Das
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.8591.50000 0001 2322 4988Institute of Global Health, University of Geneva, Geneva, Switzerland
| | - Ranitha Vongpromek
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Thanawat Assawariyathipat
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Ketsanee Srinamon
- grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Kalynn Kennon
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kasia Stepniewska
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Aniruddha Ghose
- grid.414267.20000 0004 5929 0882Chittagong Medical College (CMC), Chattogram, Bangladesh
| | - Abdullah Abu Sayeed
- grid.414267.20000 0004 5929 0882Chittagong Medical College (CMC), Chattogram, Bangladesh
| | | | - Rebeca Linhares Abreu Netto
- grid.418153.a0000 0004 0486 0972Fundação de Medicina Tropical Dr Heitor Vieira Dourado, Manaus, Amazonas Brazil
| | - Andre Siqueira
- grid.418068.30000 0001 0723 0931Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Serge R. Yerbanga
- Institut Des Sciences Et Techniques (INSTech), Bobo-Dioulasso, Burkina Faso
| | | | - James J. Callery
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Thomas J. Peto
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Rupam Tripura
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | | | - Francine Ntoumi
- grid.452468.90000 0004 7672 9850Fondation Congolaise Pour La Recherche Médicale (FCRM), Brazzaville, Congo
| | - John Michael Ong’echa
- grid.33058.3d0000 0001 0155 5938Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
| | - Bernhards Ogutu
- grid.33058.3d0000 0001 0155 5938Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
| | - Prakash Ghimire
- grid.80817.360000 0001 2114 6728Tribhuvan University, Kathmandu, Nepal
| | - Jutta Marfurt
- grid.1043.60000 0001 2157 559XGlobal and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT Australia
| | - Benedikt Ley
- grid.1043.60000 0001 2157 559XGlobal and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT Australia
| | - Amadou Seck
- grid.8191.10000 0001 2186 9619Faculty of Medicine, University Cheikh Anta Diop (UCAD), Dakar, Senegal
| | - Magatte Ndiaye
- grid.8191.10000 0001 2186 9619Faculty of Medicine, University Cheikh Anta Diop (UCAD), Dakar, Senegal
| | - Bhavani Moodley
- grid.416657.70000 0004 0630 4574Parasitology Reference Laboratory, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
| | - Lisa Ming Sun
- grid.416657.70000 0004 0630 4574Parasitology Reference Laboratory, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa
| | - Laypaw Archasuksan
- grid.10223.320000 0004 1937 0490Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Stephane Proux
- grid.10223.320000 0004 1937 0490Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Sam L. Nsobya
- grid.11194.3c0000 0004 0620 0548Department of Pathology, College of Health Science, Makerere University, Kampala, Uganda ,grid.463352.50000 0004 8340 3103Infectious Diseases Research Collaboration (IDRC), Kampala, Uganda
| | - Philip J. Rosenthal
- grid.266102.10000 0001 2297 6811University of California, San Francisco, CA USA
| | | | | | - Courosh Mehanian
- Global Health Labs, Bellevue, WA USA ,grid.170202.60000 0004 1936 8008University of Oregon, Eugene, OR USA
| | | | | | | | - Ric N. Price
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand ,grid.1043.60000 0001 2157 559XGlobal and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT Australia
| | - Arjen M. Dondorp
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - François Chappuis
- grid.150338.c0000 0001 0721 9812Division of Tropical and Humanitarian Medicine, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Philippe J. Guérin
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mehul Dhorda
- grid.499581.8Infectious Diseases Data Observatory (IDDO), Oxford, UK ,WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.501272.30000 0004 5936 4917Faculty of Tropical Medicine, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
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Karah N, Antypas K, Al-toutanji A, Suveyd U, Rafei R, Haraoui LP, Elamin W, Hamze M, Abbara A, Rhoads DD, Pantanowitz L, Uhlin BE. Teleclinical Microbiology: An Innovative Approach to Providing Web-Enabled Diagnostic Laboratory Services in Syria. Am J Clin Pathol 2022; 157:554-560. [PMID: 34643678 PMCID: PMC8973258 DOI: 10.1093/ajcp/aqab160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/19/2021] [Indexed: 12/03/2022] Open
Abstract
OBJECTIVES Telemedicine can compensate for the lack of health care specialists in response to protracted humanitarian crises. We sought to assess the usability of a teleclinical microbiology (TCM) program to provide diagnostic services in a hard-to-reach region of Syria. METHODS A semimobile station was equipped with conventional micrograph and macrograph digital imaging systems. An electronic platform (Telemicrobiology in Humanitarian Crises, TmHC) was created to facilitate sharing, interpreting, and storing the results. A pilot study was conducted to identify the bacterial species and antimicrobial susceptibility pattern of 74 urinary clinical isolates. An experience survey was conducted to capture the feedback of 8 participants in the program. RESULTS The TmHC platform (https://sdh.ngo/tmhc/) enabled systematic transmission of the laboratory records and co-interpretation of the results. The isolates were identified as Escherichia coli (n = 61), Klebsiella pneumoniae (n = 12), and Proteus mirabilis(n = 1). All the isolates were multidrug resistant. The performance of our TCM module was rated 4 (satisfying) and 5 (very satisfying) by 6 and 2 users, respectively. Data security of and cost-effectiveness were the main perceived concerns. CONCLUSIONS Although we encountered several context-related obstacles, our TCM program managed to reach a highly vulnerable population of 4 million people confined in the northwest region of Syria.
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Affiliation(s)
- Nabil Karah
- Department of Molecular Biology and Umeå Centre for Microbial Research, Umeå University, Umeå, Sweden
| | | | - Anas Al-toutanji
- Biochemical Science and Technology Department, Gaziantep Üniversitesi, Gaziantep, Turkey
| | - Usama Suveyd
- Zooteknik Department, Çukurova Üniversitesi, Gaziantep, Turkey
| | - Rayane Rafei
- Laboratoire Microbiologie Santé et Environnement, Doctoral School of Sciences and Technology, Faculty of Public Health, Lebanese University, Tripoli, Lebanon
| | - Louis-Patrick Haraoui
- Department of Microbiology and Infectious Diseases, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Canada
| | - Wael Elamin
- G42 Healthcare, Abu Dhabi, United Arab Emirates
- Queen Mary UniversityLondon, London, UK
| | - Monzer Hamze
- Laboratoire Microbiologie Santé et Environnement, Doctoral School of Sciences and Technology, Faculty of Public Health, Lebanese University, Tripoli, Lebanon
| | - Aula Abbara
- Department of Infection, Imperial College, London, UK
| | - Daniel D Rhoads
- Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH, USA
| | | | - Bernt Eric Uhlin
- Department of Molecular Biology and Umeå Centre for Microbial Research, Umeå University, Umeå, Sweden
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43
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Automatic Recognition of Ragged Red Fibers in Muscle Biopsy from Patients with Mitochondrial Disorders. Healthcare (Basel) 2022; 10:healthcare10030574. [PMID: 35327052 PMCID: PMC8949467 DOI: 10.3390/healthcare10030574] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/14/2022] [Accepted: 03/17/2022] [Indexed: 11/16/2022] Open
Abstract
Mitochondrial dysfunction is considered to be a major cause of primary mitochondrial myopathy in children and adults, as reduced mitochondrial respiration and morphological changes such as ragged red fibers (RRFs) are observed in muscle biopsies. However, it is also possible to hypothesize the role of mitochondrial dysfunction in aging muscle or in secondary mitochondrial dysfunctions. The recognition of true histological patterns of mitochondrial myopathy can avoid unnecessary genetic investigations. The aim of our study was to develop and validate machine-learning methods for RRF detection in light microscopy images of skeletal muscle tissue. We used image sets of 489 color images captured from representative areas of Gomori’s trichrome-stained tissue retrieved from light microscopy images at a 20× magnification. We compared the performance of random forest, gradient boosting machine, and support vector machine classifiers. Our results suggested that the advent of scanning technologies, combined with the development of machine-learning models for image classification, make neuromuscular disorders’ automated diagnostic systems a concrete possibility.
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44
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An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis. J Imaging 2022; 8:jimaging8030066. [PMID: 35324621 PMCID: PMC8951236 DOI: 10.3390/jimaging8030066] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 01/27/2023] Open
Abstract
Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the P. falciparum stages of life and (iii) evaluating the robustness of the models with cross-dataset experiments. Eleven well-known convolutional neural networks on two public datasets have been exploited. The results show that the networks have great accuracy in binary classification, even though they lack few samples per class. Moreover, the cross-dataset experiments exhibit the need for some further regulations. In particular, ResNet-18 achieved up to 97.68% accuracy in the binary classification, while DenseNet-201 reached 99.40% accuracy on the multiclass classification. The cross-dataset experiments exhibit the limitations of deep learning approaches in such a scenario, even though combining the two datasets permitted DenseNet-201 to reach 97.45% accuracy. Naturally, this needs further investigation to improve the robustness. In general, DenseNet-201 seems to offer the most stable and robust performance, offering as a crucial candidate to further developments and modifications. Moreover, the mobile-oriented architectures showed promising and satisfactory performance in the classification of malaria parasites. The obtained results enable extensive improvements, specifically oriented to the application of object detectors for type and stage of life recognition, even in mobile environments.
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45
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Zhang C, Jiang H, Jiang H, Xi H, Chen B, Liu Y, Juhas M, Li J, Zhang Y. Deep Learning for Microscopic Examination of Protozoan Parasites. Comput Struct Biotechnol J 2022; 20:1036-1043. [PMID: 35284048 PMCID: PMC8886013 DOI: 10.1016/j.csbj.2022.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/16/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022] Open
Abstract
The infectious and parasitic diseases represent a major threat to public health and are among the main causes of morbidity and mortality. The complex and divergent life cycles of parasites present major difficulties associated with the diagnosis of these organisms by microscopic examination. Deep learning has shown extraordinary performance in biomedical image analysis including various parasites diagnosis in the past few years. Here we summarize advances of deep learning in the field of protozoan parasites microscopic examination, focusing on publicly available microscopic image datasets of protozoan parasites. In the end, we summarize the challenges and future trends, which deep learning faces in protozoan parasite diagnosis.
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46
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Immunomolecular response of CD4+, CD8+, TNF-α and IFN-γ in Myxobolus-infected koi (Cyprinus carpio) treated with probiotics. AQUACULTURE AND FISHERIES 2022. [DOI: 10.1016/j.aaf.2022.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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47
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Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks. BIOLOGICAL IMAGING 2022; 1:e2. [PMID: 35036920 PMCID: PMC8724263 DOI: 10.1017/s2633903x21000015] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/24/2021] [Accepted: 07/14/2021] [Indexed: 12/14/2022]
Abstract
Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.
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48
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An Automatic Procedure for Overheated Idler Detection in Belt Conveyors Using Fusion of Infrared and RGB Images Acquired during UGV Robot Inspection. ENERGIES 2022. [DOI: 10.3390/en15020601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Complex mechanical systems used in the mining industry for efficient raw materials extraction require proper maintenance. Especially in a deep underground mine, the regular inspection of machines operating in extremely harsh conditions is challenging, thus, monitoring systems and autonomous inspection robots are becoming more and more popular. In the paper, it is proposed to use a mobile unmanned ground vehicle (UGV) platform equipped with various data acquisition systems for supporting inspection procedures. Although maintenance staff with appropriate experience are able to identify problems almost immediately, due to mentioned harsh conditions such as temperature, humidity, poisonous gas risk, etc., their presence in dangerous areas is limited. Thus, it is recommended to use inspection robots collecting data and appropriate algorithms for their processing. In this paper, the authors propose red-green-blue (RGB) and infrared (IR) image fusion to detect overheated idlers. An original procedure for image processing is proposed, that exploits some characteristic features of conveyors to pre-process the RGB image to minimize non-informative components in the pictures collected by the robot. Then, the authors use this result for IR image processing to improve SNR and finally detect hot spots in IR image. The experiments have been performed on real conveyors operating in industrial conditions.
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49
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Marques G, Ferreras A, de la Torre-Diez I. An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:28061-28078. [PMID: 35368860 PMCID: PMC8964254 DOI: 10.1007/s11042-022-12624-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/03/2021] [Accepted: 02/09/2022] [Indexed: 05/05/2023]
Abstract
UNLABELLED Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne transmissible infection that affects humans and other animals. According to World Health Organization (WHO), 1.5 billion malaria cases and 7.6 million related deaths have been prevented from 2000 to 2019. Malaria is a disease that can be treated if early detected. We propose a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN). The proposed model is based on EfficientNetB0 architecture. The results are validated with 10-fold stratified cross-validation. This paper presents the classification findings using images from malaria patients and normal patients. The proposed approach is compared and outperforms the related work. Furthermore, the proposed ensemble method shows a recall value of 98.82%, a precision value of 97.74%, an F1-score of 98.28% and a ROC value of 99.76%. This work suggests that EfficientNet is a reliable architecture for automatic medical diagnostics of malaria. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11042-022-12624-6.
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Affiliation(s)
- Gonçalo Marques
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Antonio Ferreras
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Isabel de la Torre-Diez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
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50
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Erythrocyte morphological symmetry analysis to detect sublethal trauma in shear flow. Sci Rep 2021; 11:23566. [PMID: 34876652 PMCID: PMC8651737 DOI: 10.1038/s41598-021-02936-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 11/24/2021] [Indexed: 11/29/2022] Open
Abstract
The viscoelastic properties of red blood cells (RBC) facilitate flexible shape change in response to extrinsic forces. Their viscoelasticity is intrinsically linked to physical properties of the cytosol, cytoskeleton, and membrane-all of which are highly sensitive to supraphysiological shear exposure. Given the need to minimise blood trauma within artificial organs, we observed RBC in supraphysiological shear through direct visualisation to gain understanding of processes leading to blood damage. Using a custom-built counter-rotating shear generator fit to a microscope, healthy red blood cells (RBC) were directly visualised during exposure to different levels of shear (10-60 Pa). To investigate RBC morphology in shear flow, we developed an image analysis method to quantify (a)symmetry of deforming ellipsoidal cells-following RBC identification and centroid detection, cell radius was determined for each angle around the circumference of the cell, and the resultant bimodal distribution (and thus RBC) was symmetrically compared. While traditional indices of RBC deformability (elongation index) remained unaltered in all shear conditions, following ~100 s of exposure to 60 Pa, the frequency of asymmetrical ellipses and RBC fragments/extracellular vesicles significantly increased. These findings indicate RBC structure is sensitive to shear history, where asymmetrical morphology may indicate sublethal blood damage in real-time shear flow.
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