3,029 research outputs found
AI-Driven Personalised Offloading Device Prescriptions: A Cutting-Edge Approach to Preventing Diabetes-Related Plantar Forefoot Ulcers and Complications
Diabetes-related foot ulcers and complications are a significant concern for
individuals with diabetes, leading to severe health implications such as
lower-limb amputation and reduced quality of life. This chapter discusses
applying AI-driven personalised offloading device prescriptions as an advanced
solution for preventing such conditions. By harnessing the capabilities of
artificial intelligence, this cutting-edge approach enables the prescription of
offloading devices tailored to each patient's specific requirements. This
includes the patient's preferences on offloading devices such as footwear and
foot orthotics and their adaptations that suit the patient's intention of use
and lifestyle. Through a series of studies, real-world data analysis and
machine learning algorithms, high-risk areas can be identified, facilitating
the recommendation of precise offloading strategies, including custom orthotic
insoles, shoe adaptations, or specialised footwear. By including
patient-specific factors to promote adherence, proactively addressing pressure
points and promoting optimal foot mechanics, these personalised offloading
devices have the potential to minimise the occurrence of foot ulcers and
associated complications. This chapter proposes an AI-powered Clinical Decision
Support System (CDSS) to recommend personalised prescriptions of offloading
devices (footwear and insoles) for patients with diabetes who are at risk of
foot complications. This innovative approach signifies a transformative leap in
diabetic foot care, offering promising opportunities for preventive healthcare
interventions.Comment: 33 pages, 2 figure
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated
directly from the Blood pressure (BP). Hypertension always leads to other
health complications. Continuous monitoring of BP is very important; however,
cuff-based BP measurements are discrete and uncomfortable to the user. To
address this need, a cuff-less, continuous and a non-invasive BP measurement
system is proposed using Photoplethysmogram (PPG) signal and demographic
features using machine learning (ML) algorithms. PPG signals were acquired from
219 subjects, which undergo pre-processing and feature extraction steps. Time,
frequency and time-frequency domain features were extracted from the PPG and
their derivative signals. Feature selection techniques were used to reduce the
computational complexity and to decrease the chance of over-fitting the ML
algorithms. The features were then used to train and evaluate ML algorithms.
The best regression models were selected for Systolic BP (SBP) and Diastolic BP
(DBP) estimation individually. Gaussian Process Regression (GPR) along with
ReliefF feature selection algorithm outperforms other algorithms in estimating
SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively.
This ML model can be implemented in hardware systems to continuously monitor BP
and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Pneumonia is a life-threatening disease, which occurs in the lungs caused by
either bacterial or viral infection. It can be life-endangering if not acted
upon in the right time and thus an early diagnosis of pneumonia is vital. The
aim of this paper is to automatically detect bacterial and viral pneumonia
using digital x-ray images. It provides a detailed report on advances made in
making accurate detection of pneumonia and then presents the methodology
adopted by the authors. Four different pre-trained deep Convolutional Neural
Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for
transfer learning. 5247 Bacterial, viral and normal chest x-rays images
underwent preprocessing techniques and the modified images were trained for the
transfer learning based classification task. In this work, the authors have
reported three schemes of classifications: normal vs pneumonia, bacterial vs
viral pneumonia and normal, bacterial and viral pneumonia. The classification
accuracy of normal and pneumonia images, bacterial and viral pneumonia images,
and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3%
respectively. This is the highest accuracy in any scheme than the accuracies
reported in the literature. Therefore, the proposed study can be useful in
faster-diagnosing pneumonia by the radiologist and can help in the fast airport
screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with
arXiv:2003.1314
A low-cost closed-loop solar tracking system based on the sun position algorithm
Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac's (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency. - 2019 Muhammad E. H. Chowdhury et al.The publication of this article was funded by the Qatar National Library. The authors would like to thank Qatar University for granting the student grant (QUST--CENG-SPR\2017-23) which made this work possible. We would like to thank the Mechanical Engineering Department, Qatar University, for their assistance in designing the mechanical system.Scopu
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Coronavirus disease (Covid-19) has been the main agenda of the whole world
since it came in sight in December 2019. It has already caused thousands of
causalities and infected several millions worldwide. Any technological tool
that can be provided to healthcare practitioners to save time, effort, and
possibly lives has crucial importance. The main tools practitioners currently
use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction
(RT-PCR) and Computed Tomography (CT), which require significant time,
resources and acknowledged experts. X-ray imaging is a common and easily
accessible tool that has great potential for Covid-19 diagnosis. In this study,
we propose a novel approach for Covid-19 recognition from chest X-ray images.
Despite the importance of the problem, recent studies in this domain produced
not so satisfactory results due to the limited datasets available for training.
Recall that Deep Learning techniques can generally provide state-of-the-art
performance in many classification tasks when trained properly over large
datasets, such data scarcity can be a crucial obstacle when using them for
Covid-19 detection. Alternative approaches such as representation-based
classification (collaborative or sparse representation) might provide
satisfactory performance with limited size datasets, but they generally fall
short in performance or speed compared to Machine Learning methods. To address
this deficiency, Convolution Support Estimation Network (CSEN) has recently
been proposed as a bridge between model-based and Deep Learning approaches by
providing a non-iterative real-time mapping from query sample to ideally sparse
representation coefficient' support, which is critical information for class
decision in representation based techniques.Comment: 10 page
Automated quantification of penile curvature using artificial intelligence
Objective: To develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.
Materials and methods: Nine 3D-printed penile models with differing curvature angles (ranging from 18 to 88°) were used to compile a 900-image dataset featuring multiple camera positions, inclination angles, and background/lighting conditions. The proposed framework of PC angle estimation consisted of three stages: automatic penile area localization, shaft segmentation, and curvature angle estimation. The penile model images were captured using a smartphone camera and used to train and test a Yolov5 model that automatically cropped the penile area from each image. Next, an Unet-based segmentation model was trained, validated, and tested to segment the penile shaft, before a custom Hough-Transform-based angle estimation technique was used to evaluate degree of PC.
Results: The proposed framework displayed robust performance in cropping the penile area [mean average precision (mAP) 99.4%] and segmenting the shaft [Dice Similarity Coefficient (DSC) 98.4%]. Curvature angle estimation technique generally demonstrated excellent performance, with a mean absolute error (MAE) of just 8.5 when compared with ground truth curvature angles.
Conclusions: Considering current intra- and inter-surgeon variability of PC assessments, the framework reported here could significantly improve precision of PC measurements by surgeons and hypospadiology researchers.Special thanks for Dr. Carlos Villanueva for providing us with the 3D printed penile models with pre-defined angulations utilized in -. Open Access Fund fees were supported by Qatar National Library.Scopu
RamanNet: A generalized neural network architecture for Raman Spectrum Analysis
Raman spectroscopy provides a vibrational profile of the molecules and thus
can be used to uniquely identify different kind of materials. This sort of
fingerprinting molecules has thus led to widespread application of Raman
spectrum in various fields like medical dignostics, forensics, mineralogy,
bacteriology and virology etc. Despite the recent rise in Raman spectra data
volume, there has not been any significant effort in developing generalized
machine learning methods for Raman spectra analysis. We examine, experiment and
evaluate existing methods and conjecture that neither current sequential models
nor traditional machine learning models are satisfactorily sufficient to
analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt
to mix the best of both worlds and propose a novel network architecture
RamanNet. RamanNet is immune to invariance property in CNN and at the same time
better than traditional machine learning models for the inclusion of sparse
connectivity. Our experiments on 4 public datasets demonstrate superior
performance over the much complex state-of-the-art methods and thus RamanNet
has the potential to become the defacto standard in Raman spectra data analysi
An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control
In the 34 developed and 156 developing countries, there are about 132 million
disabled people who need a wheelchair constituting 1.86% of the world
population. Moreover, there are millions of people suffering from diseases
related to motor disabilities, which cause inability to produce controlled
movement in any of the limbs or even head.The paper proposes a system to aid
people with motor disabilities by restoring their ability to move effectively
and effortlessly without having to rely on others utilizing an eye-controlled
electric wheelchair. The system input was images of the users eye that were
processed to estimate the gaze direction and the wheelchair was moved
accordingly. To accomplish such a feat, four user-specific methods were
developed, implemented and tested; all of which were based on a benchmark
database created by the authors.The first three techniques were automatic,
employ correlation and were variants of template matching, while the last one
uses convolutional neural networks (CNNs). Different metrics to quantitatively
evaluate the performance of each algorithm in terms of accuracy and latency
were computed and overall comparison is presented. CNN exhibited the best
performance (i.e. 99.3% classification accuracy), and thus it was the model of
choice for the gaze estimator, which commands the wheelchair motion. The system
was evaluated carefully on 8 subjects achieving 99% accuracy in changing
illumination conditions outdoor and indoor. This required modifying a motorized
wheelchair to adapt it to the predictions output by the gaze estimation
algorithm. The wheelchair control can bypass any decision made by the gaze
estimator and immediately halt its motion with the help of an array of
proximity sensors, if the measured distance goes below a well-defined safety
margin.Comment: Accepted for publication in Sensor, 19 Figure, 3 Table
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