Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease

[EN] The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.This work was financially supported by INBIO 2019 (DEEPBRAIN), INNVA1/2020/83(DEEPPET) funded by Generalitat Valenciana, and PID2019-107790RB-C22 funded by MCIN/AEI/10.13039/501100011033/. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Prats-Climent, J.; Gandia-Ferrero, MT.; Torres-Espallardo, I.; Álvarez-Sanchez, L.; Martinez-Sanchis, B.; Cháfer-Pericás, C.; Gómez-Rico, I.... (2022). Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease. Journal of Medical Systems. 46(8):1-13. https://doi.org/10.1007/s10916-022-01836-w11346

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Measurement of Higgs boson properties in the diphoton decay channel and a search for di-Higgs production in the gamma gamma b anti-b final state with the ATLAS detector

La búsqueda del bosón de Higgs fue la pieza central de los programas de física para los experimentos en el Gran Colisionador de Hadrones durante el Run 1 de la toma de datos. El descubrimiento de esta partícula, anunciado el 4 de julio de 2012 por las colaboraciones de ATLAS y CMS, representó un hito a la hora de comprender el mecanismo de ruptura de simetría electrodébil, por el cual las partículas fundamentales adquieren masa. Ahora es esencial que el bosón de Higgs sea ampliamente estudiado. Las mediciones precisas de sus propiedades confirmarán su naturaleza y cualquier desviación de la predicción del Modelo Estándar representará un signo claro de nueva física. Esta tesis presenta dos análisis de física realizados con el detector ATLAS en el Gran Colisionador de Hadrones. Se utilizaron datos de colisiones protón-protón, correspondientes a una luminosidad integrada de 36.1 1/fb, obtenida con una energía de centro de masa de 13 TeV, durante 2015 y 2016. El primer análisis es una búsqueda de la producción de pares de bosones de Higgs resonantes y no resonantes en el estado final gamma gamma b anti-b. No se observan desviaciones significativas de las predicciones del Modelo Estándar. El límite superior (con el 95% CL) observado (esperado) en la sección eficaz para la producción no resonante es de 0.73 pb (0.93 pb) y corresponde a 22 (28) veces la sección eficaz predicha por el Modelo Estándar, lo que mejora el resultado no resonante anterior obtenido por ATLAS con datos del Run 2 en un factor de cinco. Para la producción resonante de pares de bosones de Higgs decayendo a gamma gamma b anti-b, se presenta un límite en función de la masa de resonancia. Los límites observados (esperados) oscilan entre 1.14 pb (0.90 pb) y 0.12 pb (0.15 pb) para masas de resonancia en el rango de 260 GeV hasta 1000 GeV. El segundo análisis de física es la medición de las secciones eficaces totales del modo de producción del bosón de Higgs, las intensidades de la señal y las secciones eficaces simplificadas, así como la medición de las secciones eficaces fiduciales y diferenciales en el canal de desintegración del bosón de Higgs a dos fotones. La intensidad de la señal medida confirma la medición de la intensidad de la señal obtenida por ATLAS en el mismo canal de desintegración con datos del Run 1 en aproximadamente un factor de dos de mejora en cada componente de la incertidumbre. La precisión de estas mediciones actualmente está dominada por sus incertidumbres estadísticas, pero se espera que mejore en los próximos años del LHC, ya que a medida que se recopilen más datos, la incertidumbre estadística disminuirá. Se hace especial hincapié en la estrategia seguida para estimar la incertidumbre en el modelado de la lluvia de partones, el evento subyacente y la hadronización, lo cual es especialmente desafiante debido a la dificultad de generar eventos suficientes para cada categoría de reconstrucción de eventos, región fiducial o cada intervalo de una sección eficaz diferencial fiducial. Las técnicas de reconstrucción e identificación de los objetos relevantes, como fotones y jets, se cubren ampliamente, y se realiza una validación de la escala de energía del calorímetro utilizando la respuesta del calorímetro hadrónico de ATLAS, TileCal, a hadrones individuales y cargados con datos de colisiones protón-protón obtenidos con una energía del centro de masas de 7 y 8 TeV, durante 2010-2012 con el detector ATLAS. Los resultados presentados en esta tesis muestran que el cociente doble del valor medio () entre los datos y la simulación de Monte Carlo es aproximadamente uno, con desviaciones de la unidad de menos del 5% posiblemente debidas a una mala calibración de la escala electromagnética en los datos o a diferencias en la descripción de Monte Carlo debido a un desarrollo de cascada hadrónica relativamente complejo. En la región de barril del calorímetro el nivel de acuerdo del 3% se mantiene a pesar de los cambios considerables en las condiciones del haz./ La recerca del bosó de Higgs fou l’objectiu principal dels programes de física dels experiments de l’LHC durant el Run 1. El descobriment d’aquesta partícula, anunciat l’any 2012 per les col·laboracions ATLAS i CMS, constituí una fita molt important per a la física de partícules a l’hora d’entendre el mecanisme de trencament espontani de simetria del Model Estàndard pel qual les partícules fonamentals adquireixen massa. Ara és essencial que el bosó de Higgs siga estudiat extensivament. Mesures precises de les seues propietats confirmaran la seua naturalesa, i qualsevol desviació de la predicció del Model Estàndard representarà un signe inequívoc de nova física. Aquest fi no es pot aconseguir sense un bon enteniment de l’aparell experimental. Els estudis de rendiment descrits en aquesta tesi se centren en validar els mètodes de reconstrucció i calibratge del calorímetre TileCal del detector ATLAS mitjançant l’ús de la resposta del calorímetre als hadrons aïllats amb dades recollides des del 2010 fins al 2012. Els resultats mostren que el quocient doble del valor mitjà () entre les dades i la simulació de MC és compatible amb la unitat. Concretament en la regió de barril del TileCal s’observa un 3% de discrepància màxima a pesar de canvis importants en les condicions del feix al llarg dels tres anys. La producció de parells de bosons de Higgs és el procés de producció més senzill que és sensible a l’autoacoblament i proporciona una gran quantitat de possibilitats per investigar interaccions multidimensionals, així com l’existència d’estats més pesats acoblats al Higgs. Aquesta tesi presenta una recerca de la producció de parells de bosons de Higgs en l’estat final gamma gamma b anti-b amb dades recollides a una energia del centre de massa de 13 TeV amb el detector ATLAS. No s’observen desviacions significatives de les prediccions del Model Estàndard. El límit superior observat (esperat) amb un nivell de confiança del 95% en la secció eficaç de producció no ressonant és 0.73 pb (0.93 pb) i correspon a 22 (28) vegades la predicció del Model Estàndard, el qual millora el resultat precedent publicat per l’experiment ATLAS en un factor de cinc. En el cas de la producció ressonant, els límits observats (esperats) en la secció eficaç oscil·len entre 1.14 (0.90) pb i 0.12 (0.15) pb per a ressonàncies de massa entre 260 GeV i 1000 GeV. Aquesta tesi també presenta la mesura de les seccions eficaces dels modes de producció del bosó de Higgs, la força dels senyals i les seccions eficaces fiducials i diferencials en el canal de desintegració a dos fotons. La força del senyal mesurada confirma la mesura realitzada per l’experiment ATLAS amb dades recollides durant el Run 1 i la millora en un factor de dos en cada component de la incertesa. Actualment la precisió d’aquestes mesures està dominada per les seues incerteses estadístiques, però s’espera que millori en els propers anys de l’LHC, ja que a mesura que es recopilin més dades la incertesa estadística disminuirà. Aleshores, ser capaços de realitzar estimacions precises de les incerteses sistemàtiques serà essencial, en particular de la incertesa en la modelització de la pluja de partons, l’esdeveniment subjacent i l’hadronització, que suposa un repte a causa de la dificultat de generar esdeveniments suficients per a cada categoria de reconstrucció, regió fiducial o interval d’una secció eficaç diferencial. Aquesta tesi estableix els fonaments per a l’estimació d’aquesta incertesa que s’espera millorar en un futur pròxim.The hunt for the Higgs boson was the centerpiece of the physics programs for the experiments at the Large Hadron Collider during Run 1 of data-taking. The discovery of this particle, announced on July 4th 2012 by the ATLAS and CMS collaborations, represented a milestone in clarifying the mechanism of electroweak symmetry breaking, by which fundamental particles acquire mass. It is now essential that the Higgs boson is extensively studied. Precise measurements of its properties will confirm its nature, and any deviations from the Standard Model prediction will represent a clear sign of new physics. This thesis presents two physics analyses performed with the ATLAS detector at the Large Hadron Collider. Proton-proton collision data was used, corresponding to an integrated luminosity of 36.1 1/fb, obtained at a center-of-mass energy of 13 TeV, during 2015 and 2016. The first analysis is a search for resonant and non-resonant Higgs boson pair production in the gamma gamma b anti-b final state. No significant deviations from the Standard Model predictions are observed. The observed (expected) 95% CL upper limit on the cross section for non-resonant production is 0.73 pb (0.93 pb) and corresponds to 22 (28) times the predicted SM cross section, which improves the previous ATLAS Run 2 non-resonant result in a factor of five. For resonant production of di-Higgs to gamma gamma b anti-b, a limit is presented for the narrow-width approximation as a function of the resonance mass. The observed (expected) limits range between 1.14 pb (0.90 pb) and 0.12 pb (0.15 pb) for resonance masses in the range from 260 GeV until 1000 GeV. The second physics analysis is the measurement of the total Higgs boson production-mode cross sections, signal strengths, and simplified template cross sections, as well as the measurement of the fiducial and differential cross sections in the diphoton decay channel. The measured signal strength confirms the ATLAS Run 1 diphoton signal strength measurement with around a factor of two improvement in each component of the uncertainty. The precision of these measurements is currently dominated by their statistical uncertainties, but it is expected to improve in the next years of the LHC, as more data is collected the statistical uncertainty will decrease. Special emphasis is given to the strategy followed to estimate the uncertainty in the modeling of the parton shower, underlying event and hadronization, which is especially challenging due to the difficulty of generating sufficient events for each event reconstruction category, fiducial region, or each bin of a fiducial differential cross section. The reconstruction and identification techniques of the relevant objects such as photons and jets are covered extensively, and a validation of the calorimeter energy scale is performed by using the ATLAS Tile Calorimeter response to single hadrons with proton-proton collision data obtained at center-of-mass energies of 7 and 8 TeV, during 2010-2012 with the ATLAS detector. Results presented in this thesis show that the double ratio of the mean value () between data and MC simulation is approximately one, with deviations from unity of less than 5% possibly due to poor electromagnetic scale calibration in the data or differences in the MC description due to a relatively complex hadron shower development. In the Long Barrel region, the 3% level agreement is maintained despite sizeable changes in beam conditions

A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor

MAIC–10 brief quality checklist for publications using artificial intelligence and medical images

Key points AI solutions have become an essential clinical tool in medical imaging. Standardised criteria are necessary to ensure quality of AI studies. Established criteria are often incomplete, too exhaustive, or not broadly applicable. A concise and reproducible quantitative checklist will help to ensure a minimum of acceptance

A Gadolinium(III) Complex Based on Pyridoxine Molecule with Single-Ion Magnet and Magnetic Resonance Imaging Properties

Pyridoxine (pyr) is a versatile molecule that forms part of the family of B vitamins. It is used to treat and prevent vitamin B6 deficiency and certain types of metabolic disorders. Moreover, the pyridoxine molecule has been investigated as a suitable ligand toward metal ions. Nevertheless, the study of the magnetic properties of metal complexes containing lanthanide(III) ions and this biomolecule is unexplored. We have synthesized and characterized a novel pyridoxine-based GdIII complex of formula [GdIII(pyr)2(H2O)4]Cl3 · 2 H2O (1) [pyr = pyridoxine]. 1 crystallizes in the triclinic system and space group Pī. In its crystal packing, cationic [Gd(pyr)2(H2O)4]3+ entities are connected through H-bonding interactions involving non-coordinating water molecules and chloride anions. In addition, Hirshfeld surfaces of 1 were calculated to further investigate their intermolecular interactions in the crystal lattice. Our investigation of the magnetic properties of 1, through ac magnetic susceptibility measurements, reveals the occurrence of a slow relaxation in magnetization in this mononuclear GdIII complex, indicating an unusual single-ion magnet (SIM) behavior for this pseudo-isotropic metal ion at very low temperatures. We also studied the relaxometric properties of 1, as a potential contrast agent for high-field magnetic resonance imaging (MRI), from solutions of 1 prepared in physiological serum (0.0–3.2 mM range) and measured at 3 T on a clinical MRI scanner. The values of relaxivity obtained for 1 are larger than those of some commercial MRI contrast agents based on mononuclear GdIII systems

The role of intimate partner violence perpetrators' resting state functional connectivity in treatment compliance and recidivism

Abstract To expand the scientific literature on how resting state functional connectivity (rsFC) magnetic resonance imaging (MRI) (or the measurement of the strength of the coactivation of two brain regions over a sustained period of time) can be used to explain treatment compliance and recidivism among intimate partner violence (IPV) perpetrators. Therefore, our first aim was to assess whether men convicted of IPV (n = 53) presented different rsFC patterns from a control group of non-violent (n = 47) men. We also analyzed if the rsFC of IPV perpetrators before staring the intervention program could explain treatment compliance and recidivism one year after the intervention ended. The rsFC was measured by applying a whole brain analysis during a resting period, which lasted 45 min. IPV perpetrators showed higher rsFC in the occipital brain areas compared to controls. Furthermore, there was a positive association between the occipital pole (OP) and temporal lobes (ITG) and a negative association between the occipital (e.g., occipital fusiform gyrus, visual network) and both the parietal lobe regions (e.g., supramarginal gyrus, parietal operculum cortex, lingual gyrus) and the putamen in IPV perpetrators. This pattern was the opposite in the control group. The positive association between many of these occipital regions and the parietal, frontal, and temporal regions explained treatment compliance. Conversely, treatment compliance was also explained by a reduced rsFC between the rostral prefrontal cortex and the frontal gyrus and both the occipital and temporal gyrus, and between the temporal and the occipital and cerebellum areas and the sensorimotor superior networks. Last, the enhanced rsFC between the occipital regions and both the cerebellum and temporal gyrus predicted recidivism. Our results highlight that there are specific rsFC patterns that can distinguish IPV perpetrators from controls. These rsFC patterns could be useful to explain treatment compliance and recidivism among IPV perpetrators

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Abstract Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area. Key points • Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata. • Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data. • Developing a common data model for storing all relevant information is a challenge. • Trust of data providers in data sharing initiatives is essential. • An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area