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Biomedical Signal and Image Processing with Artificial Intelligence

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 14717

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Dr. Bhanu Prakash Kn

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A-Star, Singapore Bioimaging Consortium, Singapore City, Singapore
Interests: biomedical signal and image analysis; pattern recognition; AI; clinical decision support systems; differential diagnosis

Special Issue Information

Dear Colleagues,

This Special Issue of “Biomedical signal and Image Processing with Artificial Intelligence” focuses on applications of advanced techniques of AI for Biomedical Signal and Image processing. In the era of IOT, 5G, AI/ML/DL, Cloud computing, Big Data, and Precision Medicine, we hope to provide an opportunity and platform for researchers to publish their work.

Advancements in AI/ML/DL applications for biomedical signal and image processing has accelerated biomedical research, leading to acceptance of software as a medical device (SaMD) in the clinical setting. There are many AI products which have been approved by FDA and the investment into AI in Healthcare is booming. Precision medicine needs multimodal (imaging and non-imaging) information fusion and data analytics where AI plays a key role. Additionally, biomedical data analytics play a crucial role in clinical diagnosis, treatment planning, management of the condition, differential diagnosis and prognosis. We invite articles to this Special Issue that cover all aspects of AI/ML/DL for:

Biomedical signal and image acquisition;
Feature extraction and characterization;
Clinical decision support systems;
Radiomics;
New AI architectures;
Quantum processing.

Dr. Bhanu Prakash Kn
Guest Editor

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Published Papers (7 papers)

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Research

12 pages, 5341 KiB

Open AccessArticle

Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed

by Gary Garcia-Molina

Sensors 2024, 24(1), 96; https://doi.org/10.3390/s24010096 - 24 Dec 2023

Viewed by 750

Abstract

The ability to monitor blood pressure unobtrusively and continuously, even during sleep, may promote the prevention of cardiovascular diseases, enable the early detection of cardiovascular risk, and facilitate the timely administration of treatment. Publicly available data from forty participants containing synchronously recorded signals from four force sensors (load cells located under each leg of a bed) and continuous blood pressure waveforms were leveraged in this research. The focus of this study was on using a deep neural network with load-cell data as input composed of three recurrent layers to reconstruct blood pressure (BP) waveforms. Systolic (SBP) and diastolic (DBP) blood pressure values were estimated from the reconstructed BP waveform. The dataset was partitioned into training, validation, and testing sets, such that the data from a given participant were only used in a single set. The BP waveform reconstruction performance resulted in an R² of 0.61 and a mean absolute error < 0.1 mmHg. The estimation of the mean SBP and DBP values was characterized by Bland–Altman-derived limits of agreement in intervals of [−11.99 to 15.52 mmHg] and [−7.95 to +3.46 mmHg], respectively. These results may enable the detection of abnormally large or small variations in blood pressure, which indicate cardiovascular health degradation. The apparent contrast between the small reconstruction error and the limit-of-agreement width owes to the fact that reconstruction errors manifest more prominently at the maxima and minima, which are relevant for SBP and DBP estimation. While the focus here was on SBD and DBP estimation, reconstructing the entire BP waveform enables the calculation of additional hemodynamic parameters. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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19 pages, 1979 KiB

Open AccessArticle

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

by Rachel Proffitt, Mengxuan Ma and Marjorie Skubic

Sensors 2023, 23(18), 7872; https://doi.org/10.3390/s23187872 - 14 Sep 2023

Cited by 1 | Viewed by 841

Abstract

Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients’ health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants’ homes over three months. Given the extensive amount of data, only a portion of the participants’ data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional–de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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30 pages, 2902 KiB

Open AccessArticle

NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

by Giuseppe Giacopelli, Michele Migliore and Domenico Tegolo

Sensors 2023, 23(10), 4598; https://doi.org/10.3390/s23104598 - 09 May 2023

Cited by 1 | Viewed by 1855

Abstract

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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15 pages, 523 KiB

Open AccessArticle

Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI

by Pierluigi Carcagnì, Marco Leo, Marco Del Coco, Cosimo Distante and Andrea De Salve

Sensors 2023, 23(3), 1694; https://doi.org/10.3390/s23031694 - 03 Feb 2023

Cited by 11 | Viewed by 2997

Abstract

Alzheimer’s disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can help in the early detection of associated cognitive impairment. The aim of this work is to improve the automatic detection of dementia in MRI brain data. For this purpose, we used an established pipeline that includes the registration, slicing, and classification steps. The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResNet, DenseNet, and EfficientNet) and two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnosis. To allow a fair comparison, the experiments were performed on two publicly available datasets (ADNI and OASIS) using multiple benchmarks obtained by changing the number of slices per subject extracted from the available 3D voxels. The experiments showed that very deep ResNet and DenseNet models performed better than the shallow ResNet and VGG versions tested in the literature. It was also found that transformer architectures, and DeiT in particular, produced the best classification results and were more robust to the noise added by increasing the number of slices. A significant improvement in accuracy (up to 7%) was achieved compared to the leading state-of-the-art approaches, paving the way for the use of CAD approaches in real-world applications. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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18 pages, 740 KiB

Open AccessArticle

Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis

by Diogo Azevedo, Ana Maria Rodrigues, Helena Canhão, Alexandra M. Carvalho and André Souto

Sensors 2023, 23(3), 1219; https://doi.org/10.3390/s23031219 - 20 Jan 2023

Cited by 2 | Viewed by 2183

Abstract

The normalized compression distance (NCD) is a similarity measure between a pair of finite objects based on compression. Clustering methods usually use distances (e.g., Euclidean distance, Manhattan distance) to measure the similarity between objects. The NCD is yet another distance with particular characteristics that can be used to build the starting distance matrix for methods such as hierarchical clustering or K-medoids. In this work, we propose Zgli, a novel Python module that enables the user to compute the NCD between files inside a given folder. Inspired by the CompLearn Linux command line tool, this module iterates on it by providing new text file compressors, a new compression-by-column option for tabular data, such as CSV files, and an encoder for small files made up of categorical data. Our results demonstrate that compression by column can yield better results than previous methods in the literature when clustering tabular data. Additionally, the categorical encoder shows that it can augment categorical data, allowing the use of the NCD for new data types. One of the advantages is that using this new feature does not require knowledge or context of the data. Furthermore, the fact that the new proposed module is written in Python, one of the most popular programming languages for machine learning, potentiates its use by developers to tackle problems with a new approach based on compression. This pipeline was tested in clinical data and proved a promising computational strategy by providing patient stratification via clusters aiding in precision medicine. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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26 pages, 4452 KiB

Open AccessArticle

LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases

by Prajoy Podder, Sanchita Rani Das, M. Rubaiyat Hossain Mondal, Subrato Bharati, Azra Maliha, Md Junayed Hasan and Farzin Piltan

Sensors 2023, 23(1), 480; https://doi.org/10.3390/s23010480 - 02 Jan 2023

Cited by 18 | Viewed by 2708

Abstract

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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16 pages, 1007 KiB

Open AccessArticle

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

by Xinchen Fan, Lancheng Zou, Ziwu Liu, Yanru He, Lian Zou and Ruan Chi

Sensors 2022, 22(10), 3661; https://doi.org/10.3390/s22103661 - 11 May 2022

Cited by 4 | Viewed by 1954

Abstract

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human–computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net. Full article

(This article belongs to the Special Issue Biomedical Signal and Image Processing with Artificial Intelligence)

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