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Cheng W, Liu J, Wang C, Jiang R, Jiang M, Kong F. Application of image recognition technology in pathological diagnosis of blood smears. Clin Exp Med 2024; 24:181. [PMID: 39105953 PMCID: PMC11303489 DOI: 10.1007/s10238-024-01379-z] [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: 04/21/2024] [Accepted: 05/13/2024] [Indexed: 08/07/2024]
Abstract
Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.
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Affiliation(s)
- Wangxinjun Cheng
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Jingshuang Liu
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Chaofeng Wang
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Ruiyin Jiang
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Mei Jiang
- Department of Clinical Laboratory, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
| | - Fancong Kong
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
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Witharana P, Chang L, Maier R, Ogundimu E, Wilkinson C, Athanasiou T, Akowuah E. Feasibility study of rehabilitation for cardiac patients aided by an artificial intelligence web-based programme: a randomised controlled trial (RECAP trial)-a study protocol. BMJ Open 2024; 14:e079404. [PMID: 38688664 PMCID: PMC11086203 DOI: 10.1136/bmjopen-2023-079404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION Cardiac rehabilitation (CR) delivered by rehabilitation specialists in a healthcare setting is effective in improving functional capacity and reducing readmission rates after cardiac surgery. It is also associated with a reduction in cardiac mortality and recurrent myocardial infarction. This trial assesses the feasibility of a home-based CR programme delivered using a mobile application (app). METHODS The Rehabilitation through Exercise prescription for Cardiac patients using an Artificial intelligence web-based Programme (RECAP) randomised controlled feasibility trial is a single-centre prospective study, in which patients will be allocated on a 1:1 ratio to a home-based CR programme delivered using a mobile app with accelerometers or standard hospital-based rehabilitation classes. The home-based CR programme will employ artificial intelligence to prescribe exercise goals to the participants on a weekly basis. The trial will recruit 70 patients in total. The primary objectives are to evaluate participant recruitment and dropout rates, assess the feasibility of randomisation, determine acceptability to participants and staff, assess the rates of potential outcome measures and determine hospital resource allocation to inform the design of a larger randomised controlled trial for clinical efficacy and health economic evaluation. Secondary objectives include evaluation of health-related quality of life and 6 minute walk distance. ETHICS AND DISSEMINATION RECAP trial received a favourable outcome from the Berkshire research ethics committee in September 2022 (IRAS 315483).Trial results will be made available through publication in peer-reviewed journals and presented at relevant scientific meetings. TRIAL REGISTRATION NUMBER ISRCTN97352737.
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Affiliation(s)
- Pasan Witharana
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Lisa Chang
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
| | - Rebecca Maier
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
| | | | - Christopher Wilkinson
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
- Hull York Medical School, University of York, York, UK
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Enoch Akowuah
- Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
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Tsebriy O, Khomiak A, Miguel-Blanco C, Sparkes PC, Gioli M, Santelli M, Whitley E, Gamo FJ, Delves MJ. Machine learning-based phenotypic imaging to characterise the targetable biology of Plasmodium falciparum male gametocytes for the development of transmission-blocking antimalarials. PLoS Pathog 2023; 19:e1011711. [PMID: 37801466 PMCID: PMC10584170 DOI: 10.1371/journal.ppat.1011711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/18/2023] [Accepted: 09/25/2023] [Indexed: 10/08/2023] Open
Abstract
Preventing parasite transmission from humans to mosquitoes is recognised to be critical for achieving elimination and eradication of malaria. Consequently developing new antimalarial drugs with transmission-blocking properties is a priority. Large screening campaigns have identified many new transmission-blocking molecules, however little is known about how they target the mosquito-transmissible Plasmodium falciparum stage V gametocytes, or how they affect their underlying cell biology. To respond to this knowledge gap, we have developed a machine learning image analysis pipeline to characterise and compare the cellular phenotypes generated by transmission-blocking molecules during male gametogenesis. Using this approach, we studied 40 molecules, categorising their activity based upon timing of action and visual effects on the organisation of tubulin and DNA within the cell. Our data both proposes new modes of action and corroborates existing modes of action of identified transmission-blocking molecules. Furthermore, the characterised molecules provide a new armoury of tool compounds to probe gametocyte cell biology and the generated imaging dataset provides a new reference for researchers to correlate molecular target or gene deletion to specific cellular phenotype. Our analysis pipeline is not optimised for a specific organism and could be applied to any fluorescence microscopy dataset containing cells delineated by bounding boxes, and so is potentially extendible to any disease model.
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Affiliation(s)
| | | | | | - Penny C. Sparkes
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
| | | | | | - Edgar Whitley
- Department of Management, London School of Economics and Political Science, London, United Kingdom
| | | | - Michael J. Delves
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
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Kumar S, Arif T, Ahamad G, Chaudhary AA, Khan S, Ali MAM. An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5. Diagnostics (Basel) 2023; 13:2978. [PMID: 37761346 PMCID: PMC10527934 DOI: 10.3390/diagnostics13182978] [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: 05/10/2023] [Revised: 06/11/2023] [Accepted: 06/15/2023] [Indexed: 09/29/2023] Open
Abstract
Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasite detection remains the gold standard procedure for diagnosing parasite cysts or eggs. This approach is costly, time-consuming (30 min per sample), highly tedious, and requires a specialist. However, computer vision, based on deep learning, has made great strides in recent years. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore these techniques' potential in parasitology, specifically for intestinal parasites. This research presents a novel proposal for state-of-the-art transfer learning architecture for the detection and classification of intestinal parasite eggs from images. The ultimate goal is to ensure prompt treatment for patients while also alleviating the burden on experts. Our approach comprised two main stages: image pre-processing and augmentation in the first stage, and YOLOv5 algorithms for detection and classification in the second stage, followed by performance comparison based on different parameters. Remarkably, our algorithms achieved a mean average precision of approximately 97% and a detection time of only 8.5 ms per sample for a dataset of 5393 intestinal parasite images. This innovative approach holds tremendous potential to form a solid theoretical basis for real-time detection and classification in routine clinical examinations, addressing the increasing demand and accelerating the diagnostic process. Our research contributes to the development of cutting-edge technologies for the efficient and accurate detection of intestinal parasite eggs, advancing the field of medical imaging and diagnosis.
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Affiliation(s)
- Satish Kumar
- Department of Information Technology, BGSB University, Rajouri 185131, India
| | - Tasleem Arif
- Department of Information Technology, BGSB University, Rajouri 185131, India
| | - Gulfam Ahamad
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri 185131, India
| | - Anis Ahmad Chaudhary
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
| | - Salahuddin Khan
- Department of Biochemistry, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
| | - Mohamed A. M. Ali
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
- Department of Biochemistry, Faculty of Science, Ain Shams University, Cairo 11566, Egypt
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Amin J, Sharif M, Mallah GA, Fernandes SL. An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification. Front Public Health 2022; 10:969268. [PMID: 36148344 PMCID: PMC9486170 DOI: 10.3389/fpubh.2022.969268] [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: 06/14/2022] [Accepted: 08/03/2022] [Indexed: 01/25/2023] Open
Abstract
Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.
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Affiliation(s)
- Javeria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan,*Correspondence: Javeria Amin
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ghulam Ali Mallah
- Department of Computer Science, Shah Abdul Latif University, Khairpur, Pakistan
| | - Steven L. Fernandes
- Department of Computer Science, Design and Journalism, Creighton University, Omaha, NE, United States
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