Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

XXII International Conference on Mechanics in Medicine and Biology - Abstracts Book

This book contain the abstracts presented the XXII ICMMB, held in Bologna in September 2022. The abstracts are divided following the sessions scheduled during the conference

Applying novel machine learning technology to optimize computer-aided detection and diagnosis of medical images

The purpose of developing Computer-Aided Detection (CAD) schemes is to assist physicians (i.e., radiologists) in interpreting medical imaging findings and reducing inter-reader variability more accurately. In developing CAD schemes, Machine Learning (ML) plays an essential role because it is widely used to identify effective image features from complex datasets and optimally integrate them with the classifiers, which aims to assist the clinicians to more accurately detect early disease, classify disease types and predict disease treatment outcome. In my dissertation, in different studies, I assess the feasibility of developing several novel CAD systems in the area of medical imaging for different purposes. The first study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features to predict the likelihood of cases being malignant. CADx scheme is applied to pre-process mammograms, generate two image maps in the frequency domain using discrete cosine transform and fast Fourier transform, compute bilateral image feature differences from left and right breasts, and apply a support vector machine (SVM) method to predict the likelihood of the case being malignant. This study demonstrates the feasibility of developing a new global image feature analysis based CADx scheme of mammograms with high performance. This new CADx approach is more efficient in development and potentially more robust in future applications by avoiding difficulty and possible errors in breast lesion segmentation. In the second study, to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, I investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. To this purpose, a computer-aided image processing scheme is applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, an embedded LLP algorithm optimizes the feature space and regenerates a new operational vector with 4 features using a maximal variance approach. This study demonstrates that applying the LPP algorithm effectively reduces feature dimensionality, and yields higher and potentially more robust performance in predicting short-term breast cancer risk. In the third study, to more precisely classify malignant lesions, I investigate the feasibility of applying a random projection algorithm to build an optimal feature vector from the initially CAD-generated large feature pool and improve the performance of the machine learning model. In this process, a CAD scheme is first applied to segment mass regions and initially compute 181 features. An SVM model embedded with the feature dimensionality reduction method is then built to predict the likelihood of lesions being malignant. This study demonstrates that the random project algorithm is a promising method to generate optimal feature vectors to improve the performance of machine learning models of medical images. The last study aims to develop and test a new CAD scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia. To this purpose, the CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an essential role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia. In summary, I developed and presented several image pre-processing algorithms, feature extraction methods, and data optimization techniques to present innovative approaches for quantitative imaging markers based on machine learning systems in all these studies. The studies' simulation and results show the discriminative performance of the proposed CAD schemes on different application fields helpful to assist radiologists on their assessments in diagnosing disease and improve their overall performance

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

Early diagnosis of disorders based on behavioural shifts and biomedical signals

There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life. Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing. This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.Jendearen portaeran eragin zuzena duten gaixotasun ugari daude. Hala ere, askotan, gaixotasuna pairatzen duten pertsonak ez dira euren egoerataz ohartzen, eta familiarteko edo lankideek identifikatu ohi dute berau jokabide aldaketetaz ohartzean. Portaera aldaketa hauek nabarmentzean, ordea, beranduegi izan ohi da eta atzerazeinak diren kalteak eraginda egon ohi dira. Osasun kalte larriak eta gehiegizko kostuak ekiditeko eta gaixoen bizi kalitatea hobetzeko gakoa, gaixotasuna garaiz detektatzea da. Gaur egun, etengabe zabaltzen ari den Nonahiko Konputazioaren paradigmari esker, erabiltzaileen portaera ereduak era diskretu batean monitorizatu daitezke, gailu teknologikoekin izandako interakzioari esker. Eskuartean dugun arazoari konponbidea emateko teknologi hau erabiltzeak gaixotasunen sorrera eta aurrerapena nonahi eta era diskretu batean monitorizatzeko gai diren sistemak garatzea ekarriko luke, hauek garaiz hautematea ahalbidetuz. Gaixotasun konkretu batzuentzat soluzioak proposatu izan dira teknologi honetan oinarrituz, baina metodologia orokor bat, gaixotasun sorta zabal baten detekzio goiztiarrerako erabilgarria izango dena, oraindik ez da aurkeztu. Tesi honek hutsune hori betetzea du helburu, mota honetako gaixotasunak garaiz hautemateko, era diskretu batean atzitutako datu fisiologiko eta konportamentalen erabileran oinarritzen den behaketa sistema orokor bat proposatuz. Proposatutako metodologia bi balidazio egoera desberdinen arteko analisi gurutzatu baten emaitza da: estresa lantokian eta Alzheimerra etxean, balidazio egoera bakoitzari dagozkion ekarpenak ere ondorioztatu ahal izan direlarik. Bi kasuen arteko antzekotasun eta desberdintasunen analisiak, gaixotasun berri bat nonahiko konputazioan oinarrituta detektatzeko jarraitu beharreko pausoak bere osotasunean eta era orokor batean definitzea ahalbidetu du

Advancing clinical evaluation and diagnostics with artificial intelligence technologies

Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between dif- ferent biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy- pertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, reveal- ing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female ath- letes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural con- trol response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS). Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest- ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt- takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð- um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein- kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er tengd saman við tvískoðun. Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein- og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu einkasniði. Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan- legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs- stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum þyngdupunktsins á meðan tilraunin stendur og sýna mög

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine