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This paper introduces a versatile framework crucial for robotic applications such as object manipulation, mobile robot navigation, and pole climbing. It addresses the identification of geometric shapes and dimensions of diverse objects found in varied environments. The proposed method utilizes LiDAR scanning to capture objects from different angles, generating point clouds merged through transformations and superimpositions. After filtering and slicing, intersections are isolated and projected onto a chosen datum plane. The framework employs Non-Linear Least Square fitting via Gauss Newton iterative approach, utilizing pseudo-inverse Jacobian of a hypotrochoid to approximate polygons. The algorithm consecutively fits polygon prisms, determining the best fit with the least norm of error. Results indicate an average least square error of less than 9% for radius fitting and a high f-score for shape identification. 

This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models. By drawing on these insights and addressing the challenges posed by small, single-institutional datasets, the paper aims to demonstrate how AI applications can improve diagnostic precision, enhance clinical decision-making, and ultimately lead to better patient outcomes in managing sellar region cystic lesions. 

Abstract Bio-inspired soft-robots are nowadays found their place in many applications due to its flexibility, compliance and adaptivity to unstructured environment. The main intricate part of such bio-inspired soft robots are soft pneumatic actuators (SPA) which replicate or mimic the limbs and muscles. The soft actuators are pneumatically actuated and provide bending motion in most cases. However, many engineering and medical applications need axially expanding soft pneumatic actuators to deal with delicate objects. Various studies have put forward designs for SPA with axial deformation, but the majority of them have limited axial deformation, constraining motion and less overall efficacy which limit the scope of utilization. The common practice to enhance the axial deformation of SPA is by incorporating directionally customized reinforcement using fibres or by other means like yarns, fabrics, etc These types of reinforcements are generally embedded to SPA during fabrication and may not have capability for any correction or modification later on hence lack the customization. This paper presents a novel method of radial reinforcement for the enhancement of axial deformation of SPAs with provision of customization. The present study aims to enhance and/or customize the axial deformation of SPA by incorporating external and detachable reinforcement in the form of annulus shaped cap ring. The investigation encompasses the design and attachment of four distinct cap ring geometries to SPA at different locations. Experimental results affirm that cap ring reinforcement bolster the radial stiffness, curbing lateral deformation while permitting axial deformation of soft pneumatic actuators. Out of 64 distinct configurations, the one with full reinforcement, featuring four cap rings of maximum size, yields a remarkable 169% increase in pure axial deformation compared to unreinforced cases. It is also observed that by varying the number and placement locations of cap rings the pure axial deformation can be customized. This novel insight not only propels soft pneumatic actuation technology but also heralds prospects for highly agile and versatile robotic systems which can be used in medical, prosthetics, pharmaceutical and other industries. 

Background:Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning toexpedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical imagedata to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatmentof patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional NeuralNetwork (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. Methods:In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal colorfundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) therelative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model toserve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse diseasemanifestations. Results:We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted insignificantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract,glaucoma, diabetic retinopathy. Conclusion:The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework. 

This study explores cutting-edge computational technologies and intelligent methods to create realistic synthetic data, focusing on dementia-centric Magnetic Resonance Imaging (MRI) scans related to Alzheimer’s and Parkinson’s diseases. The research delves into Generative Adversarial Networks (GANs), Variational Autoencoders, and Diffusion Models, comparing their efficacy in generating synthetic MRI scans. Using datasets from Alzheimer’s and Parkinson’s patients, the study reveals intriguing findings. In the Alzheimer dataset, diffusion models produced non-dementia images with the lowest Frechet Inception Distance (FID) score at 92.46, while data-efficient GANs excelled in generating dementia images with an FID score of 178.53. In the Parkinson dataset, data-efficient GANs achieved remarkable FID scores of 102.71 for dementia images and 129.77 for non-dementia images. The study also introduces a novel aspect by incorporating a classification study, validating the generative metrics. DenseNets, a deep learning architecture, exhibited superior performance in disease detection compared to ResNets. Training both models on images generated by diffusion models further improved results, with DenseNet achieving accuracies of 80.84% and 92.42% in Alzheimer’s and Parkinson’s disease detection, respectively. The research not only presents innovative generative architectures but also emphasizes the importance of classification metrics, providing valuable insights into the synthesis and detection of neurodegenerative diseases through advanced computational techniques.