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Classification of lumbar spine disorders using large language models and MRI segmentation

BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 1-18页

作者： Dong, Rongpeng Cheng, Xueliang Kang, Mingyang Qu, Yang Second Hosp Jilin Univ Dept Spinal Surg 218 Ziqiang St Chuangchun 130041 Peoples R China

BackgroundMRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar spine disorders through the integration of MRI data, textual reports, and numerical *** segmentation quality of MRI data is evaluated using dice coefficients (cut-off: 0.92) and intersection over union (IoU) metrics (cut-off: 0.88) to ensure precise anatomical feature extraction. The CNN extracts key lumbar spine features, such as lumbar lordotic angle (LLA) and disc heights, which are tokenized as direct scalar values representing positional relationships. A data source of 28,065 patients with various disorders, including degenerative disc disease, spinal stenosis, and spondylolisthesis, is used to establish diagnostic standards. These standards are refined through post-CNN processing of MRI texture features. The BERT-based spinal LLM model integrates these CNN-extracted MRI features and numerical values through early fusion *** analysis illustrate various lumbar spine disorders and their anatomical changes. The model achieved high performance, with all key metrics nearing 0.9, demonstrating its effectiveness in classifying conditions like spondylolisthesis, herniated disc, and spinal stenosis. External validation further confirmed the model's generalizability across different populations. External validation on 514 expert-validated MRI cases further confirms the model's clinical relevance and generalizability. The BERT-based model classifies 61 combinations of lumbar spine *** BERT-based spinal LLM significantly improves the precision of lumbar spine disorder classification, supporting accurate diagnosis and treatment planning.

关键词： Lumbar spine disorders MRI data BERT-based large language model multimodal data integration Precision classification

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MIRROR MIRROR ON THE WALL... AN INTELLIGENT MULTISENSORY MIRROR FOR WELL-BEING SELF-ASSESSMENT

MIRROR MIRROR ON THE WALL... AN INTELLIGENT MULTISENSORY MIR...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Andreu-Cabedo, Yasmina Castellano, Pedro Colantonio, Sara Coppini, Giuseppe Favilla, Riccardo Germanese, Danila Giannakakis, Giorgos Giorgi, Daniela Larsson, Marcus Marraccini, Paolo Martinelli, Massimo Matuszewski, Bogdan Milanic, Matijia Pascali, Mariantonietta Pediaditis, Mattew Raccichini, Giovanni Randeberg, Lise Salvetti, Ovidio Stromberg, Tomas Univ Cent Lancashire Preston Lancs England Natl Res Council Italy Pisa Italy Linkoping Univ Linkoping Sweden Fdn Res & Technol Inst Comp Sci Iraklion Greece Norvegian Univ Sci & Technol Trondheim Norway

ISBN: (纸本)9781479970827

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

关键词： Cardio-metabolic risk unobtrusive health monitoring 3D face detection and tracking 3D morphometric analysis multispectral imaging breath analysis psycho-somatic status recognition multimodal data integration

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Analysis of the Effects of Cross-Attention and multimodal Inputs on Robotic Assembly Tasks 24

Analysis of the Effects of Cross-Attention and Multimodal In...

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24th International Conference on Control, Automation and Systems

作者： Hwang, Yuhyeon Song, Jae-Bok Korea Univ Dept Mech Engn Seoul 02841 South Korea

ISBN: (纸本)9798331517939;9788993215380

This study examines the impact of effective integration of multimodal data on the performance of robotic assembly tasks. In a multimodal system, the complementary processing of different types of data plays a crucial role in enhancing the robot's ability to solve complex problems. The experiment compared the performance of neural networks using a multi-layer perceptron (MLP) structure and a cross-attention structure for processing multimodal data, which included images, force/torque, and robot posture information. The experimental results showed that the FC-CA structure, incorporating cross-attention, exhibited improved performance in terms of success rate and assembly time. These findings suggest that the cross-attention technique holds significant potential for performance improvement in the integration and interpretation of multimodal data. This study validates the effectiveness of cross-attention for enhancing the performance of multimodal data processing tasks in robotic assembly using diverse data types.

关键词： multimodal data integration Robotic Assembly

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Integrating multimodal Patient data into Attention-Based Graph Networks for Disease Risk Prediction 22nd

Integrating Multimodal Patient Data into Attention-Based Gra...

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22nd International Conference on Artificial Intelligence in Medicine (AIME)

作者： Huang, Xiayuan Yale Univ New Haven CT 06510 USA

ISBN: (纸本)9783031665349;9783031665356

In the era of digital medicine, electronic health records (EHRs) encompass a vast array of patient data from diverse sources. Harnessing this readily available information is crucial for personalized medicine and predictive healthcare. This study explores the potential of integrating multimodal data into an attention-based graph network for enhancing disease risk prediction. Leveraging the intrinsic structure of medical data, we propose a novel method called MiGAT, which fuses information from various modalities, including clinical events and sequencing data. By applying graph attention networks (GAT), our model effectively captures the intricate inter-relationships among patients, particularly emphasizing genetic similarity. This enables our model to assess and prioritize disease risks based on the genetic closeness of patients. Results indicate a substantial improvement in predictive accuracy compared to baseline models, reaffirming the potential of our multimodal integration strategy. Our findings underscore the importance of synergizing diverse EHR data sources through advanced computational models for better-informed clinical decision-making and more effective patient care.

关键词： EHR Machine Learning Disease Risk Modeling multimodal data integration Health Informatics

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EmoStream: Real-time Emotion Prediction through Speech with Apache Flink and Kafka integration 15

EmoStream: Real-time Emotion Prediction through Speech with ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Karri, Naga Bhuvana Ponamala, Ishitha Gudla, Vijayatejaswari Vemula, Swarna Raju Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350370249

Advancements in real-time emotion recognition systems have witnessed a surge in interest, fuelled by their potential applications in diverse fields such as human computer interaction, mental health monitoring, and user experience enhancement. Existing approaches often focus on singular modalities, such as speech analysis or facial expressions. However, a comprehensive understanding of human emotion necessitates the integration of multiple modalities. This research introduces a novel approach, a multimodal Real-time Emotion Prediction System (MREPS), which combines speech analysis with streaming data processing. The system begins by capturing a continuous stream of audio data, which undergoes Speech-to-Text (STT) conversion using cloud-based services like Google Cloud Speech-to-Text. Simultaneously, facial expression recognition and physiological signal analysis can be integrated for a more comprehensive emotional profile. The real-time nature of the data necessitates the use of a robust streaming architecture, leveraging technologies such as Apache Kafka for data ingestion and Apache Flink for stream processing. A crucial component of the system is the Emotion Prediction API, a microservice housing a machine learning model for emotion classification. This model is trained on diverse datasets to ensure accuracy across various languages, cultures, and individual differences. The real-time API endpoint continuously processes the multimodal input, providing dynamic emotion predictions. © 2024 IEEE.

关键词： Apache Flink Apache Kafka Cloud-based services Docker Emotion Prediction API Facial expression recognition Human-computer interaction Kubernetes Machine learning model Mental health monitoring Microservices multimodal data integration multimodal emotion prediction system Physiological signal analysis Privacy Real-time emotion recognition Security measures Speech analysis Speech-to Text Streaming architecture User feedback

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Modeling invariant object processing based on tight integration of simulated and empirical data in a Common Brain Space

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2012年第2012期6卷 12-12页

作者： Peters, Judith C. Reithler, Joel Goebel, Rainer Inst Royal Netherlands Acad Arts & Sci KNAW Dept Neuroimaging & Neuromodeling Netherlands Inst Neurosci NL-1105 BA Amsterdam Netherlands Maastricht Univ Dept Cognit Neurosci Fac Psychol & Neurosci Maastricht Netherlands Maastricht Univ M BIC Maastricht Netherlands

Recent advances in Computer Vision and Experimental Neuroscience provided insights into mechanisms underlying invariant object recognition. However, due to the different research aims in both fields models tended to evolve independently. A tighter integration between computational and empirical work may contribute to cross-fertilized development of (neurobiologically plausible) computational models and computationally defined empirical theories, which can be incrementally merged into a comprehensive brain model. After reviewing theoretical and empirical work on invariant object perception, this article proposes a novel framework in which neural network activity and measured neuroimaging data are interfaced in a common representational space. This enables direct quantitative comparisons between predicted and observed activity patterns with in and across multiple stages of object processing, which may help to clarify how high-order invariant representations are created from low level features. Given the advent of columnar-level imaging with high-resolution fMRI, it is time to capitalize on this new window into the brain and test which predictions of the various object recognition models are supported by this novel empirical evidence.

关键词： object perception view-invariant object recognition neuroimaging large-scale neuromodeling (high-field) fMRI multimodal data integration

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The Impact of Enriching Electroencephalogram in Spatial Metadata on Interpretability and Generalization Ability of Graph Neural Networks

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第4期34卷 1255-1263页

作者： Sidorov, L. S. Maysuradze, A. I. Lomonosov Moscow State Univ Moscow 119991 Russia

This paper proposes a formalization of current approaches to processing electroencephalograms, as well as a generalization of the original problem of processing multidimensional time series in terms of graph neural networks. The proposed architecture exploits information about the recording device by encoding information about the relative position of the recording electrodes as a graph. The effectiveness of the method has been demonstrated in tasks of emotion recognition, as well as P300 impulse recognition. The model achieved outstanding results on the considered datasets using the same set of hyperparameters. The experiments conducted demonstrate the usefulness of graphs composed of metainformation, as well as the dependence of the structure of the optimal graph on the problem being solved. Potentially, this technique can be extended to other subject areas where additional information about the connectivity of multivariate time series channels is available.

关键词： multimodal data integration multivariate time series electroencephalogram spatial data deep learning graph neural network P300 wave emotion recognition

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iCEMIGE: integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

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World Journal of Clinical Oncology 2022年第7期13卷 616-629页

作者： Xuan-Yu Mao Jesus Perez-Losada Mar Abad Marta Rodríguez-González Cesar A Rodríguez Jian-Hua Mao Hang Chang Division of Biological Systems and Engineering Lawrence Berkeley National LaboratoryBerkeley94720United States Instituto de Biología Molecular y Celular del Cáncer Universidad de SalamancaSalamanca 37007Spain Department of Pathology Universidad de SalamancaSalamanca 37007Spain Department of Medical Oncology Universidad de SalamancaSalamanca 37007Spain

BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision *** To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)*** We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)*** Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic *** was built upon the sparse representation learning *** iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS *** models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA *** We identified 39 CMBs that were used to create a CMPS system in ***,GEPS,and MAPS were found to be significantly independently associated with *** then established an iCEMIGE scoring system for risk stratification of BC *** iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular ***,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS *** Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.

关键词： Breast cancer Gene signature Microbiome signature Cellular morphometrics signature multimodal data integration Prognosis

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Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

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JHEP REPORTS 2022年第4期4卷 100443-100443页

作者： Nam, David Chapiro, Julius Paradis, Valerie Seraphin, Tobias Paul Kather, Jakob Nikolas Yale Sch Med Dept Radiol & Biomed Imaging Sect Intervent Radiol New Haven CT USA Univ Paris Ctr Rech inflammat CRI INSERM U1149 Paris France Univ Paris Hop Beaujon AP HP Dept Pathol Clichy France Heinrich Heine Univ Dusseldorf Univ Hosp Dusseldorf Med Fac Dept Gastroenterol Hepatol & Infect Dis Dusseldorf Germany Univ Hosp RWTH Aachen Dept Med 3 Aachen Germany Univ Leeds Leeds Inst Med Res St Jamess Pathol & Data Analyt Leeds England Univ Hosp Heidelberg Natl Ctr Tumor Dis NCT Med Oncol Heidelberg Germany RWTH Univ Hosp Dept Med 3 D-52074 Aachen Germany

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of meta-bolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quanti-tative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hep-atology and outline critical obstacles which need to be overcome.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY license (http://***/licenses/ by/4.0/).

关键词： Artificial intelli-gence deep learning machine learning diagnostic support system imaging multimodal data integration

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Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

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CANCER CELL 2023年第5期41卷 871-886.e10页

作者： Gaglia, Giorgio Burger, Megan L. Ritch, Cecily C. Rammos, Danae Dai, Yang Crossland, Grace E. Tavana, Sara Z. Warchol, Simon Jaeger, Alex M. Naranjo, Santiago Coy, Shannon Nirmal, Ajit J. Krueger, Robert Lin, Jia-Ren Pfister, Hanspeter Sorger, Peter K. Jacks, Tyler Santagata, Sandro Harvard Med Sch Dept Syst Biol Lab Syst Pharmacol Boston MA 02115 USA Harvard Med Sch Ludwig Ctr Harvard Boston MA 02115 USA Harvard Med Sch Brigham & Womens Hosp Dept Pathol Boston MA 02115 USA MIT David H Koch Inst Integrat Canc Res Cambridge MA 02139 USA Oregon Hlth & Sci Univ Dept Cell Dev & Canc Biol Portland OR 97212 USA Oregon Hlth & Sci Univ Sch Med Div Hematol & Oncol Portland OR 97212 USA Harvard Univ Sch Engn & Appl Sci Boston MA 02134 USA MIT Dept Biol Cambridge MA 02139 USA

Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed im-aging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lympho-nets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets re-tained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.

关键词： multiplexed imaging systems biology computational biology spatial profiling CyCIF lung adenocarcinoma immunotherapy cancer vaccines spatial biology multimodal data integration

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