scholarly journals Methodology for Comprehensive Cell-Level Analysis of Wound Healing Experiments Using Artificial Intelligence

2020 ◽  
Author(s):  
Jan Oldenburg ◽  
Lisa Maletzki ◽  
Anne Strohbach ◽  
Paul H. Bellé ◽  
Stefan Siewert ◽  
...  
Keyword(s):  
Wound Healing ◽  
Deep Learning ◽  
Cell Density ◽  
Relative Error ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
Cell Velocity ◽  
Population Scale ◽  
Conventional Methods

Abstract BackgroundEndothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale.ResultsIn an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 13.2 %. In comparison, the conventional Canny method gave a relative error of 61 %. ConclusionThe results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

scholarly journals Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jan Oldenburg ◽  
Lisa Maletzki ◽  
Anne Strohbach ◽  
Paul Bellé ◽  
Stefan Siewert ◽  
...  
Keyword(s):  
Wound Healing ◽  
Deep Learning ◽  
Cell Density ◽  
Relative Error ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
Cell Velocity ◽  
Population Scale ◽  
Conventional Methods

Abstract Background Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. Results In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. Conclusion The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

scholarly journals Tensor Flow Model in Medical Image Analysis - Review

2021 ◽  
Vol 12 (5) ◽  
pp. 1389-1397
Author(s):  
Raswitha Bandi, Et. al.
Keyword(s):  
Deep Learning ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Techniques ◽  
Vector Machines ◽  
Detection Of Objects ◽  
Medical Analysis

Support Vector Machines, Reinforcement algorithms, artificial neural networks are some of the Machine Learning Algorithms available in Medical Analysis. By using these algorithms, much of the research has been done in analysis of liver cancer for genome classification and identification of lesions. At present, Deep learning algorithms have quickly turned into a strategy for examine CT images. This article presents one of the major deep learning techniques named tensor flow technique to investigate images in scan for the task of visualization of abnormal condition of liver tumor in the context of shape and color towards disease diagnosis. We surveyed the utilization of tensor flow for classifying images, detection of objects, and detection of lesions. In this paper, we mainly concentrated on the study and working of tensor flow in image classification. Also, a summary of the present and future scope in this area has been presented in detail.

Demystification of Deep Learning-Driven Medical Image Processing and Its Impact on Future Biomedical Applications

2020 ◽  
pp. 155-171
Author(s):  
R. Udendhran ◽  
Balamurugan M.
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Biomedical Applications ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
New Era ◽  
Fast Development ◽  
Computational Technology

The recent growth of big data has ushered in a new era of deep learning algorithms in every sphere of technological advance, including medicine, as well as in medical imaging, particularly radiology. However, the recent achievements of deep learning, in particular biomedical applications, have, to some extent, masked decades-long developments in computational technology for medical image analysis. The methods of multi-modality medical imaging have been implemented in clinical as well as research studies. Due to the reason that multi-modal image analysis and deep learning algorithms have seen fast development and provide certain benefits to biomedical applications, this chapter presents the importance of deep learning-driven medical imaging applications, future advancements, and techniques to enhance biomedical applications by employing deep learning.

Feasibility Study of Deep Learning Based Radiation Sensitivity Prediction Model Using Gene Expression Profiling

2021 ◽  
Author(s):  
Euidam Kim ◽  
Yoonsun Chung
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Deep Learning ◽  
Prediction Model ◽  
Relative Error ◽  
Absorbed Dose ◽  
Absolute Error ◽  
Radiation Sensitivity ◽  
Learning Technology

Abstract Since radiation sensitivity prediction can be used in various field, we investigate the feasibility of an in vitro radiation sensitivity prediction model using a deep neural network. A microarray of the National Cancer Institute-60 tumor cell lines and clonogenic surviving fraction at an absorbed dose of 2 Gy values are used to predict radiation sensitivity. The prediction model is based on convolutional neural network and 6-fold cross-validation approach is applied to validate the model. Of the 174 samples, 170 (97.7%) samples show less than 10% and 4 (2.30%) show more than 10% of relative error, respectively. Through an additional validation, model accurately predict 172 out of 174 samples, representing a prediction accuracy of 98.85% under the criteria of absolute error < 0.01 or the relative error < 10%. This results demonstrate that in vitro radiation sensitivity prediction from gene expression can be carried out with the deep learning technology.

Preadaptation of cell numbers for wound healing assays

2021 ◽  
Vol 7 (2) ◽  
pp. 295-298
Author(s):  
Jana Markhoff ◽  
Andreas Brietzke ◽  
Niels Grabow
Keyword(s):  
Cell Proliferation ◽  
Wound Healing ◽  
Cell Density ◽  
Healing Process ◽  
Cell Types ◽  
Wound Dressings ◽  
Wound Healing Process ◽  
Specific Cell ◽  
Cell Numbers

Abstract In vitro wound healing assays are a suitable application to verify the efficiency of pharmaceuticals or growth factors that will be incorporated in or immobilized to e.g. electrospun biomaterials for wound dressings or other biological devices in advance. Thereby, various factors like culture conditions or cell density influence the specific cell proliferation. Hence, to establish a wound healing assay for various cell types, a stepwise adaptation of cell numbers was done for better estimation and comparison of cell density for the validation of the influence of drugs on the wound healing process. Cell proliferation of different tissue relevant cell types was evaluated by impedance measurements and live cell imaging. Cell numbers could be successfully adapted for assay specific cell densities. In general, a universal comparison of biological or chemical materials and agents in vitro may require the creation of appropriate ISO or OECD standards for a consistent and cell specific adaptation or demand of initial cell density.

An in vitro model for investigation of vitamin A effects on wound healing

2021 ◽  
pp. 1-6
Author(s):  
Hoda Keshmiri Neghab ◽  
Mohammad Hasan Soheilifar ◽  
Gholamreza Esmaeeli Djavid
Keyword(s):  
Wound Healing ◽  
Endothelial Cell ◽  
Vitamin A ◽  
In Vitro Model ◽  
Cellular Proliferation ◽  
Endothelial Cell Migration ◽  
Normal Fibroblast ◽  
Anti Inflammatory ◽  
Vitro Model

Abstract. Wound healing consists of a series of highly orderly overlapping processes characterized by hemostasis, inflammation, proliferation, and remodeling. Prolongation or interruption in each phase can lead to delayed wound healing or a non-healing chronic wound. Vitamin A is a crucial nutrient that is most beneficial for the health of the skin. The present study was undertaken to determine the effect of vitamin A on regeneration, angiogenesis, and inflammation characteristics in an in vitro model system during wound healing. For this purpose, mouse skin normal fibroblast (L929), human umbilical vein endothelial cell (HUVEC), and monocyte/macrophage-like cell line (RAW 264.7) were considered to evaluate proliferation, angiogenesis, and anti-inflammatory responses, respectively. Vitamin A (0.1–5 μM) increased cellular proliferation of L929 and HUVEC (p < 0.05). Similarly, it stimulated angiogenesis by promoting endothelial cell migration up to approximately 4 fold and interestingly tube formation up to 8.5 fold (p < 0.01). Furthermore, vitamin A treatment was shown to decrease the level of nitric oxide production in a dose-dependent effect (p < 0.05), exhibiting the anti-inflammatory property of vitamin A in accelerating wound healing. These results may reveal the therapeutic potential of vitamin A in diabetic wound healing by stimulating regeneration, angiogenesis, and anti-inflammation responses.

In vitro anti-inflammatory and wound healing activities of Citrus auraptene

Planta Medica ◽  
2012 ◽  
Vol 78 (11) ◽  
Author(s):  
F Epifano ◽  
S Genovese ◽  
L Zhao ◽  
V Dang La ◽  
D Grenier
Keyword(s):  
Wound Healing ◽  
Anti Inflammatory

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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