Technologie en innovatie in Vlaanderen: Prioriteiten.

Vlaanderen;

A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE Transactions on Artificial Intelligenc

DicerHET primäärinen hermosoluviljemämalli miRNA-biogeneesireitin roolin tutkimiseksi Parkinsonin taudissa

Tiettyjen hermosoluryhmien vajaatoiminta ja rappeutuminen on ikään liittyville hermoston rappeumasairauksille yhteinen ilmentymä. Parkinsonin tauti (PD) on yleinen etenevä hermoston rappeumasairaus, jonka oireet pahenevat taudin edetessä. Tautia luonnehtii dopamiinia (DA) tuottavien hermosolujen asteittainen rappeutuminen sekä merkittävät proteiinikertymälöydökset, ns. Lewyn kappaleet (Lewy body; LB), jotka koostuvat lähinnä presynaptisesta proteiinista nimeltä α-synukleiini (αSyn). PD:n hoitoratkaisut painottuvat edelleen oireiden lievitykseen, sillä hermorappeumaa käynnistäviä tautimekanismeja ei vielä täysin ymmärretä. Kahden edellisen vuosikymmenen aikana mikroRNA (miRNA)-molekyylit ovat herättäneet suurta kiinnostusta eri biolääketieteen aloilla ja saaneet erityistä huomiota hermorappeumasairauksien tutkimuksessa. Viimeisimmät kehitykset viittaavat siihen, että miRNA-tasot muuttuvat sekä ikääntymiskudoksessa että monien ikään liittyvien sairauksien yhteydessä, kuten PD:ssä. Tuoreissa tutkimuksissa on myös havaittu, että Dicer:in, miRNA-molekyylien synteesireitin tärkeimmän entsyymin, ilmentyminen alentuu ikääntymisen myötä. Tätä mekanismia on pyritty jäljittelemään DicerHET hiirimallilla, joka perustuu Dicer-geenin heterotsygoottiseen mutageneesiin. Alustavissa tutkimuksissa DicerHET-malli osoittautui lupaavaksi tutkimusmalliksi häiriintyneen miRNA-synteesireitin ja PD:hen liittyvän hermorappeuman välisen yhteyden tutkimiseen. Täten, tulevien tutkimustöiden helpottamiseksi ja lääkeaineseulontojen nopeuttamiseksi, tässä työssä olemme pyrkineet tuottamaan vastaavaa DicerHET in vitro mallia soveltamalla kätevää perimänmuokkauksen menetelmää. Tavoitteena oli kelpuuttaa malli ja luoda yhdenmukainen ja toistettava menetelmä tuleviin töihin, joissa tutkitaan miRNA-biosynteesin roolia PD:n tautimekanismissa. DicerHET-genotyyppi tuotettiin soluviljelmissä, yhdistämällä perinteistä Cre/loxP-systeemiä viruksen välittämään Cre-synteesiin. Tarkemmin ottaen, aivokuoren primääriviljelmät, jotka olivat peräisin Dicer flox/+ -hiirien alkioista, transdusoitiin Cre:tä ilmentävillä lentivirusvektoreilla (lenti-hSYN-T2A-Cre) "loxp-rajatun" Dicer-alleelin poistamiseksi. Määrittääksemme menetelmälle optimaaliset parametrit, arvioimme rekombinaatiotehokkuutta eri transduktio-olosuhteissa. Optimaalisissa olosuhteissa pystyimme saavuttamaan tehokkaan rekombinaation 5 päivän induktion jälkeen viljelmissä. Immunohistokemialliset värjäykset osoittivat kuitenkin, että DicerHET -genotyyppi ei heikentänyt solujen eloonjäämistä. Havainnollistaaksemme tutkimusmallin konseptia, altistimme DicerHET-viljelmät vielä ns. ennalta muodostetuille fibrilleille (pre-formed fibrils; PFF) – tämä on PD:een liittyvä stressitekijä, joka saa αSyn-proteiinit kertymään rykelmiin. pSer129-αSyn-positiivisia LB:n kaltaisia rykelmiä havaittiin kaikissa PFF-käsitellyissä viljelmissä. Rykelmiä kertyi kuitenkin enemmän DicerHET-viljelmiin. Tämä ei kuitenkaan aiheuttanut lisääntynyttä solukuolemaa, mikä viittaa siihen, että DicerHET -genotyyppi ei lisää aivokuoren neuronien haavoittuvuutta pSer129-αSyn-kertymiä kohtaan. Aikaisempien tutkimuksiemme perusteella oletamme, että DA-hermosolut ovat erityisen herkkiä ikääntymiseen liittyvää Dicer-entsyymitason alentumista kohtaan. Tästä kiehtovasta yhteydestä olisi mahdollista saada lisänäyttöä tulevissa tutkimuksissa soveltamalla DicerHET- mallia yhtä helposti primäärisiin DA-neuroneihin.Selective degeneration and dysregulation of specific neuronal populations is a common hallmark shared by neurodegenerative diseases affecting the aging population. Parkinson’s disease (PD) is one of the most prevalent neurodegenerative diseases with debilitating clinical manifestations that follow a chronic and progressive course. Pathological hallmarks of PD involve gradual and specific loss of DA (DA) neurons and widespread presence of Lewy body (LB) inclusions that consist of aggregated presynaptic protein, α-Synuclein (αSyn). Treatment of PD remains to be at symptomatic management as the underlying mechanisms that trigger neurodegeneration are still not fully elucidated. Over the past two decades, microRNAs (miRNAs) have become a major area of interest within biomedical fields and gained increasing momentum in the context of neurodegenerative diseases. In recent developments, changes in mature miRNA profiles have been reported in aging tissue and many age-related diseases, including PD. More recently, a number of studies have found that the most essential enzyme in the miRNA biogenesis pathway, Dicer, exhibits reduced expression with aging. To these ends, a genetic mouse model based on heterozygous knockout of Dicer (DicerHET) was introduced to simulate Dicer downregulation. Initial investigations identified the DicerHET model as a promising tool for studying the relationship between disrupted miRNA biogenesis and neurodegeneration associated with PD. To facilitate future investigations and speed up screening of potential therapeutic compounds using this genetic model, in the current work, we aimed to produce a DicerHET in vitro model with a practical and convenient genetic engineering approach. The main focus of this work was to validate the model and establish a standardized reproducible approach suitable for research that addresses the role of miRNA biogenesis in PD. The desired DicerHET genotype was generated in vitro by employing traditional Cre/loxP system in conjunction with a virally mediated Cre expression. More specifically, primary cortical cultures, derived from Dicer flox/+ mice embryos, were transduced with Cre expressing lentiviral vectors (lenti-hSYN-T2A-Cre) to delete the “floxed” Dicer allele. To establish optimal parameters for the procedure, we analysed recombination efficiency under different transduction conditions. The most efficient recombination was achieved after 5 days of induction in cultures. However, we observed that DicerHET genotype did not attenuate survival of the cells, as assessed by immunohistochemistry. Further, as a proof of concept, we exposed the DicerHET cultures to pre-formed fibrils (PFFs) - a PD related stressor that causes αSyn aggregation. pSer129-αSyn-positive LB-like aggregates were detected in all the PFF-treated cultures, however, with a greater accumulation in the DicerHET cultures. Interestingly, increased aggregation was not accompanied by increased cell death, suggesting that DicerHET genotype does not increase vulnerability of cortical neurons to pSer129-αSyn aggregation. Based on our earlier studies we presume that DA neurons may bear a specific vulnerability towards the age-related Dicer depletion. More conclusive evidence on this intriguing relationship could be provided in future research using the DicerHET model that can be readily applied to primary DA cultures

Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

 With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

A NARRATIVE REVIEW OF PRECISION MEDICINE, ARTIFICIAL INTELLIGENCE, AND THE FUTURE OF PERSONALIZED CARE: REVOLUTIONIZING HEALTHCARE.

Background The merging of AI and precision medicine is a paradigm change in healthcare. Precision medicine uses patient-specific traits to adapt medical interventions, while AI improves decision-making with advanced computational approaches. This convergence considers genetic and nongenomic characteristics, patient symptoms, clinical history, and lifestyle to address precision medicine difficulties. Objective This narrative review explores the potential of AI in advancing precision medicine. It examines the synergy between AI and precision medicine, emphasizing their combined capacity to enable individualized diagnosis and prognosis for patients with unique healthcare requirements or atypical responses to treatments. Summary of Narrative Review Recent literature underscores the promise of AI in precision medicine through translational research. AI's computational power allows it to analyze vast datasets, identify patterns, and generate valuable insights. By integrating genomic and nongenomic determinants with clinical and lifestyle data, AI enhances the accuracy and effectiveness of diagnosis and prognosis. This review delves into the transformative potential of this convergence, highlighting its applications in healthcare decision-making and patient care. Implications for Future Research Future research should focus on further developing AI-driven precision medicine tools and platforms. Investigating the real-world clinical impact of AI-driven precision medicine is essential, along with evaluating the scalability, ethical considerations, and regulatory frameworks for its implementation. Additionally, exploring AI's potential in optimizing treatment plans, drug discovery, and healthcare resource allocation is a crucial avenue for future research. Clinical Practice and Policy Development The amalgamation of AI and precision medicine offers healthcare professionals augmented intelligence, empowering them to make more informed decisions tailored to individual patient needs. This has the potential to improve treatment outcomes, reduce adverse effects, and enhance patient care. Policymakers and healthcare institutions should consider investing in AI-driven precision medicine initiatives and establishing guidelines for data privacy, ethics, and patient consent to ensure its responsible and ethical implementation