Deep learning and computer vision techniques for microcirculation analysis: A review

Helmy, Maged; Truong, Trung Tuyen; Jul, Eric; Ferreira, Paulo H.

doi:10.1016/j.patter.2022.100641

Cited by 7 publications

(4 citation statements)

References 140 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The droplet area and fluorescence intensity were chosen as the key factors for quantification and processing. The images were converted to an 8-bit format to obtain grayscale maps for binarization, 43 and a neural network model was used to restore the "clusters" in the binarized image to simulate the boundaries of the microdroplets. This step enabled subsequent calculations and data analysis.…”

Section: ■ Resultsmentioning

confidence: 99%

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning

Wang,

Feng,

Zhou

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

Detection of nucleic acids from a single multiplexed and amplification-free test is critical for ensuring food safety, clinical diagnostics, and environmental monitoring. In this study, we introduced a mesophilic Argonaute protein from Clostridium butyricum (CbAgo), which exhibits nucleic acid endonuclease activity, to achieve a programmable, amplification-free system (PASS) for rapid nucleic acid quantification at ambient temperatures in one pot. By using CbAgo-mediated binding with specific guide DNA (gDNA) and subsequent targeted cleavage of wild-type target DNAs complementary to gDNA, PASS can detect multiple foodborne pathogen DNA (<10 2 CFU/mL) simultaneously. The fluorescence signals were then transferred to polydisperse emulsions and analyzed by using deep learning. This simplifies the process and increases the suitability of polydisperse emulsions compared to traditional digital PCR, which requires homogeneous droplets for accurate detection. We believe that PASS has the potential to become a next-generation point-of-care digital nucleic acid detection method.

show abstract

Section: ■ Resultsmentioning

confidence: 99%

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning

Wang,

Feng,

Zhou

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

show abstract

“…This approach was later extended to support vector regression for regression problems, which has demonstrated strong performance. Generalization performance and robustness of regression models based on the SVR approach can be enhanced ( Tang et al, 2022 ; Helmy et al, 2023 ; Ying et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Quantitative structure–activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

et al. 2023

View full text Add to dashboard Cite

The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure–activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set, R2 = 0.97 and RMSE = 0.01; in a test set, R2 = 0.95, RMSE = 0.01, and Rcv2 = 0.96. This result is in line with the experimental expectations, which indicate that the MIX-SVR modeling approach has good applications in the study of amide derivatives.

show abstract

“…These methods can identify and classify objects, resulting in recommendations or insights based on data from different systems [24]. Computer vision techniques aim to identify desired images from the surrounding background by discriminating among various image attributes, such as edges, colors, textures, corners, and other properties [25]. Through deep learning techniques that guide image processing and analysis, computer vision allows computers to understand and interpret visual information.…”

Section: Computer Visionmentioning

confidence: 99%

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Fernández Del Carpio

2024

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Computer vision has become an important technology for obtaining meaningful data from visual content and providing valuable information for enhancing security controls, marketing, and logistic strategies in diverse industrial and business sectors. The retail sector constitutes an important part of the worldwide economy. Analyzing customer data and shopping behaviors has become essential to deliver the right products to customers, maximize profits, and increase competitiveness. In-person shopping is still a predominant form of retail despite the appearance of online retail outlets. As such, in-person retail is adopting computer vision models to monitor store products and customers. This research paper presents the development of a computer vision solution by Lytica Company to detect customers in Steren’s physical retail stores in Mexico. Current computer vision models such as SSD Mobilenet V2, YOLO-FastestV2, YOLOv5, and YOLOXn were analyzed to find the most accurate system according to the conditions and characteristics of the available devices. Some of the challenges addressed during the analysis of videos were obstruction and proximity of the customers, lighting conditions, position and distance of the camera concerning the customer when entering the store, image quality, and scalability of the process. Models were evaluated with the F1-score metric: 0.64 with YOLO FastestV2, 0.74 with SSD Mobilenetv2, 0.86 with YOLOv5n, 0.86 with YOLOv5xs, and 0.74 with YOLOXn. Although YOLOv5 achieved the best performance, YOLOXn presented the best balance between performance and FPS (frames per second) rate, considering the limited hardware and computing power conditions.

show abstract

Deep learning and computer vision techniques for microcirculation analysis: A review

Cited by 7 publications

References 140 publications

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning

Quantitative structure–activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Contact Info

Product

Resources

About