A pillar-based microfluidic chip for T-cells and B-cells isolation and detection with machine learning algorithm

Turan, Bilal; Masuda, Taisuke; Wu, Lei; Noor, Anas Mohd; Horio, Koji; Saito, Toshiki; Miyata, Yasuyuki; Arai, Fumihito

doi:10.1186/s40648-018-0124-8

Cited by 13 publications

(15 citation statements)

References 24 publications

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“…The different points in the ROC's and DET curves correspond to different rates of accuracy, specificity and sensitivity. We achieved an accuracy of 98%, sensitivity of 97% and specificity of 99% in both cases using AlexCAN and AlexCAN using preselected windows, compared to the accuracy of 94%, sensitivity of 90% and specifity of 99% of our previous work [8]. In one experiment, a total of 420 images were scanned for the filtration zone on the microfluidic chip.…”

Section: Resultsmentioning

confidence: 91%

“…A fivefold cross validation is used to assess the performance. Although this process is straightforward and can be achieved with a simple color feature, we achieved a 99% accuracy rate compared to a 96% accuracy rate from our previous work which used a simpler color feature detector [8].…”

Section: Calibrating Color Distributionmentioning

confidence: 78%

“…There is no standard dataset for fluorescent cell images, so we created a custom dataset in our previous work [8]. Previously a total of 6200 cells and 35,000 background images were gathered semi-automatically.…”

Section: The Datasetmentioning

confidence: 99%

“…Moreover, its accuracy in distinguishing cells from the background is also equal to that of the previously re-trained CNN which could detect cells from the background without the type of the cell. Therefore, AlexCAN simplifies the two layer detection approach in our last work, which first detected cells from the background and then annotated them as T-cells and B-cells [8].…”

Section: Calibrating Color Distributionmentioning

confidence: 99%

“…Cell counting methods using microfluidic devices are promising substitutes for conventional flow cytometry systems since they allow for faster analysis, reduced sample volume, and are portable, inexpensive and disposable. For the isolation and detection of T-cells and B-cells from whole blood, we designed a microfluidic chip and implemented a machine learning algorithm with an appropriate feature descriptor [7,8]. Due to the variability of cells in size, morphology and color intensity, a Histogram of Oriented Gradients (HOG) descriptor, which is a popular choice for object detection among the computer vision community due to its robustness, is used [9].…”

Section: Introductionmentioning

confidence: 99%

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High accuracy detection for T-cells and B-cells using deep convolutional neural networks

Turan¹,

Masuda²,

Noor³

et al. 2018

Robomech J

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Providing an accurate count of total leukocytes and specific subsets (such as T-cells and B-cells) within small amounts of whole blood is a rather challenging ordeal due to the lack of techniques that enable the separation of leukocytes from a limited amount of whole blood. In a previous study we designed a microfluidic chip utilizing a micropillar array to isolate T-cells and B-cells from the sub-microliter of whole blood. Due to the variability of cells in size, morphology and color intensity, a Histogram of Oriented Gradients (HOG) based Support Vector Machine (SVM) classifier was proposed with an average accuracy of 94%, specificity of 99% and sensitivity of 90%. The HOG can separate the cells from the background with a high accuracy rate however, some noise is similar in shape and size to the actual cells and this results in misclassification. To alleviate this situation, in this study a convolutional neural network is trained and used to distinguish T-cells and B-cells with an accuracy rate of 98%, a specificity of 99% and a sensitivity of 97%. We also propose an HOG feature based SVM classifier to preselect the detection windows to accelerate the detection to process images in less than 10 min. The proposed on-chip cell detecting and counting method will be useful for numerous applications in diagnosis and for monitoring diseases.

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Section: Resultsmentioning

confidence: 91%

Section: Calibrating Color Distributionmentioning

confidence: 78%

Section: The Datasetmentioning

confidence: 99%

Section: Calibrating Color Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

High accuracy detection for T-cells and B-cells using deep convolutional neural networks

Turan¹,

Masuda²,

Noor³

et al. 2018

Robomech J

Self Cite

View full text Add to dashboard Cite

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Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Harofte

Soltani

Siavashy

et al. 2022

Small

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utilization of biological systems to develop new products at one end of the spectrum to the application of technology for solving the problems in biology at the other end. [1] According to the new experimental technologies like DNA nucleotide sequencing and RNA sequencing genomics, [2] Mass Spectrometry [3] as well as X-ray crystallography and Nuclear Magnetic Resonance spectroscopy [4] in proteomics, large datasets are generated and sorted in the field of biotechnology that make new opportunities for researchers along with companies that present services and products in this area. Nowadays, performing stateof-the-art research in biotechnology is roughly impossible without exploiting database and artificial intelligence (AI) technologies. [5] The integration of AI and biotechno logy is now prevalent. AI elucidates computational systems which extract signals and learn from the input. [1] Historically, AI has a close relationship with biology. Precisely, this relation is between statistics and genetics. Compared to text or image datasets, biological data is more multidimensional, complicated, and noisy. Hence, AI models are more pertinent than simple statistical models for biological datasets. [6] Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

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Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

et al. 2020

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Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nanotechnologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.

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A pillar-based microfluidic chip for T-cells and B-cells isolation and detection with machine learning algorithm

Cited by 13 publications

References 24 publications

High accuracy detection for T-cells and B-cells using deep convolutional neural networks

High accuracy detection for T-cells and B-cells using deep convolutional neural networks

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

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