SAURABH GUPTA

Purpose: Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality. 
Method: Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate. 
Result: Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI.
Conclusion: The study's findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases.

The physical and chemical interactions among the cells and scaffolds are pivotal for regenerating the desired tissue. The fields of material science and tissue engineering aim to understand this complex behaviour, which can pave new ways for optimising the tissue growth. The present study attempts to predict the in-vitro fibroblast cell growth by modelling the physico-chemical characteristics of the biopolymeric scaffolds through different supervised machine learning strategies for skin tissue engineering application. The chemical nature, porosity, surface roughness, and wettability of the chitosan and gelatin-based scaffolds were used as indicative support; and the cell growth percentage to train various regression models. The random forest classifier provided the specificity, sensitivity, and precision of 88.6%, 99.87%, and 93.75% respectively after hyperparameter tuning. The applicability and efficiency of machine learning for predicting skin tissue engineering outcomes can help in saving time, resources, and human errors while biomaterials designing.

Obstructive Sleep Apnea (OSA) is a crucial sleep-breath disorder often characterized by partial or complete cessation of airflow during sleep. The syndrome has a hazard effect on other physiological functions causing primary risk of fragmented sleep which is a part of Sleep Apnea Syndrome (SAS), headache and morning sickness. As secondary risks, there is a high chance of vehicular accidents, cardiac failure and stroke. For the diagnosis of OSA, polysomnography (PSG) is considered as the gold standard strategy that records and analyzes brain waves, heart rate, breathing pattern, oxygen level and artifacts of the survival. This paper uses a multi-modal approach by considering both ECG and SpO2 signals. Feature extractions of both the signals are carried out to extract time–frequency domain features and spatial features. Then, the extracted features are combined at a feature-level fusion technique. Finally, the fused features are fed to a series of machine learning classifiers such as Decision Tree (DT), Random Forest (RF) and Radial Basis Function-based Support Vector Machine (RBF-SVM). Comparing the classification performances, accuracy of RBF-based SVM outperformed other two classifiers with a score of 98.60%. Therefore, our proposed methodology can be considered for automated classification of OSA and non-OSA subjects.

3D bioprinting has emerged as a tool for developing in vitro tissue models for studying
disease progression and drug development. The objective of the current study was to evaluate the
influence of flow driven shear stress on the viability of cultured cells inside the luminal wall of
a serpentine network. Fluid–structure interaction was modeled using COMSOL Multiphysics for
representing the elasticity of the serpentine wall. Experimental analysis of the serpentine model
was performed on the basis of a desirable inlet flow boundary condition for which the most ho-
mogeneously distributed wall shear stress had been obtained from numerical study. A blend of
Gelatin-methacryloyl (GelMA) and PEGDA200 PhotoInk was used as a bioink for printing the serpen-
tine network, while facilitating cell growth within the pores of the gelatin substrate. Human umbilical
vein endothelial cells were seeded into the channels of the network to simulate the blood vessels. A
Live-Dead assay was performed over a period of 14 days to observe the cellular viability in the printed
vascular channels. It was observed that cell viability increases when the seeded cells were exposed
to the evenly distributed shear stresses at an input flow rate of 4.62 mm/min of the culture media,
similar to that predicted in the numerical model with the same inlet boundary condition. It leads to
recruitment of a large number of focal adhesion point nodes on cellular membrane, emphasizing the
influence of such phenomena on promoting cellular morphologies.

The relationship between Neuroscience and Arti cial Intelligence are quite intertwined and strong sine a long decades. However, in recent times, the collaboration between these two domains are building a vital role in modern medical science. The study of AI aims at making the behavior of machine more intelligence and versatile, hence it is an interesting topic to be analyzed about better understanding of biological brain by emphasizing the historical and current advances of AI. We have initiated this review by highlighting the brief taxonomy of AI. Later on the key role of AI in the eld of computational neuroscience, cognitive neuroscience, clinical neuroscience, Reinforcement learning, cognitive mapping and spatial navigation have been shared. The paper is proceeding with recent challenges faced by AI during its implication on neurobiological data and building neural model. The challenges have proposed some feasible solutions to sharpen the context of computation, learning, cognition and perception by strengthening neural network model. The progressive approach is continued towards the future of AI by conceptualizing Explainable AI, Deep Brain Stimulation and generating new codes for both Machine Learning and Deep Learning region. The scope of AI is expanding in multiple domains of medical science, engineering and technology; hence the potentiality of AI needs to be updated and polished by time.

Artificial intelligence (AI) has emerged as a useful tool for early detection of pneumonia disease in the lungs using chest X-ray (CXR). For pneumonia detection different machine learning, deep learning, and transfer learning algorithms are used but a detailed review comparing the dataset with literature is lacking. This review paper first briefly summarizes different AI-based algorithms on classification, regression, and clustering. Then a detailed comparison of current literature on the ground of different reliable datasets and techniques are presented. Lastly, major challenges faced over the last few years are discussed with their future scopes. Our main objective is to provide a state-of-the-art  review  of  the  AI  studies  detecting  pneumonia  disease  in  CXR  using  data  comparison  and  find  the  limitations  to  make suggestions for practitioner

The present work had evaluated the effect of cryogenic treatment (233 K) on the degradation of polymeric biomaterial using a numerical model. The study on effect of cryogenic temperature on mechanical properties of cell-seeded biomaterials is very limited. However, no study had reported material degradation evaluation. Different structures of silk-fibroin-poly-electrolyte complex (SFPEC) scaffolds had been designed by varying hole distance and hole diameter, with reference to existing literature. The size of scaffolds were maintained at 5 × 5 mm 2. Current study evaluates the effect of cryogenic temperature on mechanical properties (corelated to degradation) of scaffold. Six parameters related to scaffold degradation: heat transfer, deformation gradient, stress, strain, strain tensor, and displacement gradient were analyzed for three different cooling rates (− 5 K/min, − 2 K/min, and − 1 K/min). Scaffold degradation had been evaluated in the presence of water and four different concentrations of cryoprotectant solution. Heat distribution at various points (points_base, point_wall and point_core) on the region of interest (ROI) was found similar for different cooling rates of the system. Thermal stress was found developing proportional to cooling rate, which leads to minimal variation in thermal stress over time. Strain tensor was found gradually decreasing due to attenuating response of deformation gradient. In addition to that, dipping down of cryogenic temperature had prohibited the movement of molecules in the crystalline structure which had restricting the displacement gradient. It was found that uniform distribution of desired heat at different cooling rates has the ability to minimize the responses of other scaffold degradation parameters. It was found that the rates of change in stress, strain, and strain tensor were minimal at different concentrations of cryoprotectant. The present study had predicted the degradation behavior of PEC scaffold under cryogenic temperature on the basis of explicit mechanical properties.

Blood flow prediction is very important for medical diagnosis, drug development, tissue engineering, and continuous monitoring. One commonly used method for studying blood flow is called multi-exposure laser speckle contrast imaging (MECI). It provides valuable insights into how blood flows through tissues and helps in diagnosing circulatory diseases. In our study, we used MECI to measure blood flow in real-time by taking multiple measurements with different exposure times and contrasts. To predict different blood flow rates ranging from 0.1 to 1 mm/s, we employed machine learning (ML) techniques like clustering and random forest (RF) or support vector machine (SVM) algorithms. The study showed that RF with K-means performance is found to be the most accurate technique for flow classification, with an accuracy of 98.5%, a precision of 92%, a specificity of 98.9%, and a classification error of 1.5%. Our study demonstrates that employing clustering and RF algorithms in MECI provides a robust and effective approach to predicting blood flow. This technique holds great potential for a wide range of applications in the medical and healthcare fields.

Cardiologists can acquire important information related to patients’ cardiac health using carotid artery stiffness, its lumen diameter (LD), and its carotid intima-media thickness (cIMT). The sonographers primarily concern about the location of the artery in B-mode ultrasound images. Localization using manual methods is tedious and time-consuming and also may lead to some errors. On the other hand, automated approaches are more objective and can provide the localization of the artery at near real time. Above arterial parameters may be determined after localization of the artery in real time.

The segmentation procedure of thoracic CT images is essential to produce a systematic diagnosis and disease detection. There are many healthcare facilities and hospitals available worldwide where manual analysis of medical images is being performed. An increasing amount of medical images, such as thoracic CT images, making this manual procedure much complex, inefficient, and prone to error. This situation can be easily overcome by utilizing a computer-automated detection (CAD) system, which is suitable to generate an image analysis with a reduced error percentage. However, it requires an efficient segmentation technique to produce better reliable outcomes. The notion of this article is to delineate the efficient lung segmentation technique.This article describes the Convolutional neural network (CNN) based Lung nodules detection methodology. CNN is used here to learn the knowledge from a large amount of data, in order to provide improved detection mechanisms. An advanced SumNet based architecture has been incorporated along with CNN network for reducing training complexity by enhancing the learning rate. The performance score of the proposed method shows 94.11% accuracy which is more relevant for the image analysis as compared to other existing techniques. It has also achieved 0.94 score for specificity and 0.93 dice score. The training losses are also reduced to 0.10 for training and 0.05 for validation operation. After introducing SumNet architecture, it has been found that the speed of operations is improved in terms of training, testing, and validation performance. It further allows getting insights from the vast amount of data in less amount of time. This process delivers improved output parameters for lung nodule detection, and certainly, it would be preferred for the development of better CAD systems.

Schizophrenia (SCZ) characterized as chronic psychotic mental disorder which severely affects the social life of an individuals. The present study analysed the delta and gamma band using resting EEG signals in SCZ and healthy controls (HC). Total of 10 subjects were selected for each group and 19 channel rest EEG data was recorded. The relative band energy for delta and gamma band was extracted and the ratio of delta and gamma energy (DGER) was calculated as a main feature. The results showed significant difference in the DGER value at frontal and prefrontal electrode locations. The DGER value observed to be significantly low in SCZ group compared to HC group. The classification output showed maximum classification accuracy of 89% using linear discriminant classifier when classifying the SCZ and HC group using DGER. In conclusion, the present study suggests the use of DGER as useful neurophysiological marker for identification of SCZ.

A pseudo-time, sub-optimal stochastic filtering approach based on derivative free variant of ensemble Kalman filter for solving inverse problem of Ultrasound Modulated Optical Tomography is developed. The proposed scheme enhanced the contrast of reconstructed images.

The experimental results are obtained for accelerated particle filtering for Diffuse Optical Tomography. Broyden updates are used to accomplish computation of Jacobian. The proposed scheme has shown remarkable efficacy for reconstruction with data.