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etal. & Synnergren, J. Multimodal deep learning for biomedical data fusion: A review. Researchers can use the early fusion method as a first attempt to learn multimodal representations since it can learn to exploit the interactions and correlations between features of each modality. We note here that MEDLINE is covered in PubMed . Ulyana et al.48 trained a deep, fully connected network as a regressor in a 5-year longitudinal study on AD to predict cognitive test scores at multiple future time points. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. In accordance with the guidelines for scoping reviews30,31, we did not perform quality assessments of the included studies. Expert Syst. The two feature vectors were directly concatenated and fed into Bayesian MLP for classification. & 0001, Z. In this review, AD diagnosis is an example in which imaging and EHRs data are dependent as relevant and accurate knowledge of the patients current symptomatology, personal information and imaging reports can help doctors interpret imaging results in a suitable clinical context, resulting in a more precise diagnosis. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. J. Dermatol. combined MRI imaging with structured data extracted from EHRs to diagnose demyelinating diseases using SVM. Grant, D., Papie, B.W., Parsons, G., Tarassenko, L. & Mahdi, A. Intell. What is the most used method? For example, the brain MRI pixel data and the quantitative result of an MMSE (e.g., Qiu et al.37) for diagnosing MCI are independent, making them appropriate candidates for inclusion in the late fusion strategy. This is not surprising, given the privacy concerns surrounding revealing healthcare data. IEEE Signal Process. Consequently, it is imperative to encourage the sharing of flexible data among institutions and hospitals in order to facilitate the exploration of a wider range of population data for clinical research. Three models took the average of the predicted probabilities from the imaging and EHR modality models as the final prediction. Based on our study selection criteria (see Methods), 44 studies remained for full-text review after excluding articles based on their abstract and title. One author (F.M.) In these studies, EHRs were combined with medical imaging to diagnose a spectrum of diseases including neurological disorders (\(n = 9\))4,13,14,15,32,37,42,49,50, psychiatric disorders (\(n = 2\))33,36, CVD (\(n =3\))54,55,56, Cancer (\(n = 2\))16,55, and four studies for other different diseases18,19,40,53. Grant et al.55 used a Residual Network (ResNet50) architecture to extract relevant features from the imaging modality and fully connected NN to process the non-imaging data. Xu, T., Zhang, H., Huang, X., Zhang, S. & Metaxas, D.N. Multimodal deep learning for cervical dysplasia diagnosis. The study early fused all features into gradient boosting classifiers for prediction. Signals Sensors 11, 120 (2021). In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020. Also, studies that combined multi-omics data modality were excluded. Our industry-leading platform was developed with feedback from over 150,000 medical professionals, and as a result we've been recognized as the #1 EHR for Overall Performance, Reliability, Support and Customer care! Ding, S., Huang, H., Li, Z., Liu, X. Alzheimer Dis. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. In 2021 International Joint Conference on Neural Networks (IJCNN), 18 (IEEE, 2021). N. Engl. Under the early prediction group, we considered only the studies that predict diseases before onset, identify significant risk factors, predict mortality and overall survival, and predict a treatment outcome. To evaluate the performance of the modality fusion, they tested their model using a single modality of MRI and clinical features. Nevertheless, we expect and agree with26 that joint fusion models can provide better results than other fusion strategies because they update their feature representations iteratively by propagating the loss to all the feature extraction models, aiming to learn correlations across modalities. Our definition of imaging modalities is any type of medical imaging used in clinical practice, such as MRI, PET, CT scans, and Ultrasound. In addition, studies that were unrelated to the medical field or did not use AI-based models were excluded. Accessed on September 15, 2021. We first identify the EHR and medical imaging modalities that are the focus of this review. https://doi.org/10.1016/j.inffus.2020.07.006 (2020). Prediction tasks were reported in 14 (\(\sim 41.2\%\)) studies. On this page, well discuss the different information and clinical data that may appear in patients medical charts. Features include billing, scheduling, waitlists, insights, teletherapy, documentation, and more to streamline your clinic! In another research38, Fang et al. Alzheimer Dis. A. Xu, M. et al. Moreover, through this review, we observed certain trends in the field of multimodality fusion in the medical area, which can be categorized as: Resources: We observed that multimodal data resources of medical imaging and EHR are limited owing to privacy considerations. Correspondence to Accessed on September 15, 2021. The purpose of fusion techniques is to effectively take advantage of cooperative and complementary features of different modalities4,5. Int. Using ML fusion techniques consistently demonstrated improved AD diagnosis, while clinicians experience difficulty with accurate and reliable diagnosis even when multimodal data is available26. The former55 proposed a multimodal network for cardiomegaly classification, which simultaneously integrates the non-imaging intensive care unit (ICU) data (laboratory values, vital sign values, and static patient metadata, including demographics) and the imaging data (chest X-ray). They utilized CNN with a self-attention mechanism to extract the features of images, and then they concatenated them with the metadata information. It discussed fusion strategies, the clinical tasks and ML models that implemented data fusion, the type of diseases, and the publicly accessible multimodal data for medical imaging and EHRs. As presented in Table2, approximately two-thirds of the studies were journal articles (\(n = 23\), \(\sim 68\)%)12,13,14,15,17,19,25,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, whereas 11 studies were conference proceedings (\(\sim 32\)%)16,47,48,49,50,51,52,53,54,55,56. JAMA 309, 13511352 (2013). This can be seen in Alzheimers disease diagnosis and prediction12,13,14,15 when imaging data were combined with specific lab test results and demographic data as inputs to ML models, and better performance was achieved than the single-source models. To join the learned imaging and non-imaging features, they concatenated the learned feature representation and fed them into two fully connected layers to generate a label for cardiomegaly diagnosis. contracts here. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Aging (Albany NY) 12, 18151 (2020). Therefore, all AD diagnosis studies in this review implemented either early fusion13,14,15 or joint fusion49 for multimodal learning. "Fusion" refers to the joint modeling of multiple modalities at once by combining their feature embeddings; popular deep learning fusion operations include addition and concatenation. MRI and PET images were the most utilized modalities. In their study, the feature extraction part applied a ResNet architecture and MLP for CT and clinical data, respectively. J. Soc. Furthermore, their review covered the research till 2019 and retrieved only 17 studies. Moreover, we focused on the type of imaging and EHR data used by the studies, the source of data, and its availability. 42, 333345 (2015). Practice Fusion offers just that, a place doctors can store their patients' medical records for free. Monit 10, 737749 (2018). Based on this reviewed studies, early fusion models performed better than conventional single-modality models on the same task. and H.A.) Fang, C. et al. The most common applied clinical outcome in the included studies was the diagnosis, reported in 20 (\(\sim 59\%\)) studies. A review on machine learning principles for multi-view biological data integration. Then, they concatenated the learned features of the two modalities before feeding them into a stacked K-nearest neighbor (KNN) attention pooling layer. The primary purpose of our scoping review is to explore and analyze published scientific literature that fuses EHR and medical imaging using AI models. Murdoch, T. B. NPJ Digit. For acute ischemic stroke, Gianluca et al.35 evaluated the predictive power of imaging, clinical, and angiographic features to predict the outcome of acute ischemic stroke using ML. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. The study also created early, joint fusion models and two single modality models to compare with late fusion performance. In addition, we recorded implementation details of the model, such as feature extraction and single modality evaluation. For this model, they used Bayesian CNN encoder-decoder to extract imaging features and a Bayesian MLP encoder-decoder to process the medical indicators data. 144, 105253. https://doi.org/10.1016/j.compbiomed.2022.105253 (2022). Finally, Tanveer et al.54 combined features from echocardiogram reports and images, with diagnosis information for the detection of patients with aortic stenosis CVD. Practice Fusion is the #1 cloud-based electronic health record (EHR) platform for doctors and patients in the U.S. A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder. https://doi.org/10.3233/SHTI220697 (2022). Behrad, F. & Saniee Abadeh, M. An overview of deep learning methods for multimodal medical data mining. Finally, Bai et al.52 compared different multimodal biomarkers (clinical data, biochemical and hemologic parameters, and ultrasound elastography parameters) for predicting the assessment of fibrosis in chronic hepatitis B using SVM. Xu et al.53 used AlexNet architecture to convert the imaging data into a feature vector fusible with other non-image modalities. Access your health records anytime. Their study utilized VGG-11 architecture for MRI feature extraction and developed two MLP models for MMSE and LM test results. They fed the fused features vector to different ML models, including support vector machine (SVM), random forest (RF), and gaussian process (GP) for classification. Scientific Reports Radiol. Joint fusion: It combines the learned features from intermediate layers of NN with features from other modalities as inputs to a final model during training26. Qiu et al.37 trained three independent imaging models that took a single MRI slice as input, then aggregated the prediction of these models using maximum, mean, and majority voting. Arksey, H. & OMalley, L. Scoping studies: Towards a methodological framework. The fourth model used an NN classifier as an aggregator, which took as input the single modality models prediction. 18, 270277 (2004). Figure 2 shows a visualization of the publication type-wise and year-wise distribution of the studies. Trials 15, 111 (2014). San Francisco-based medical records startup Practice Fusion allegedly developed software for pharmaceutical companies to help increase the number of prescriptions doctors wrote for pain . In56, Sharma et al. Moreover, all included studies were limited to English language only. 342350 (Springer International Publishing, Cham, 2021). Scientific Reports (Sci Rep) Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). They directly concatenated the learned feature representation of the imaging and non-imaging data and fed them into two fully connected networks. & Booth, A. et al. In this review, we focus on studies that use two primary data modalities: Medical imaging modality: This includes N-dimensional imaging information acquired in clinical practice, such as X-ray, Magnetic Resonance Imaging (MRI), functional MRI (fMRI), structural MRI (sMRI), Positron Emission Tomography (PET), Computed Tomography (CT), and Ultrasound. Most of the current research directly concatenated the feature vectors of the different modalities to combine multimodal data. For small datasets, it is preferable to use early or late fusion methods as they can be implemented using classical ML techniques. Furthermore, we limited our search to English-language articles published in the last seven years between January 1, 2015, and January 6, 2022. Based on the performance reported in the included studies, it is preferred to try the early and joint fusion when the relation between the two data modalities is complementary. A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics. Artificial intelligence-based methods for fusion of electronic health records and imaging data. Liu et al.27 focused exclusively on integrating multimodal EHR data, where multimodality refers to structured data and unstructured free texts in EHR, using conventional ML and DL techniques. This bias may result in an overestimation of the benefits associated with multimodal data analysis. & Detsky, A. S. The inevitable application of big data to health care. Their model produced MMSE scores for ten unique future time points at six-month intervals by combing biomarkers from cognitive test scores, PET, and MRI. and H.A.) As a result, 13 of these studies exhibited a better performance for fusion when compared with their imaging-only and clinical-only counterparts12,13,15,16,18,25,32,33,34,41,42,43,44,51. Then, they fed the fused features into a stacked KNN pooling layer to classify the patients diagnosis data. Yidong et al.19 used a Bayesian CNN encoder-decoder to extract imaging features and a Bayesian Multilayer perception (MLP) encoder-decoder to process the medical indicators data. After that, they concatenated the extracted features of the two modalities and fed them into fully connected NN for prediction. In late fusion, multiple models are employed, each specialized in a single modality, thereby limiting the size of the input feature vector for each model. Still, most studies combine the modalities with relatively simple strategies, which despite being shown to be effective, might not fully exploit the rich information embedded in these modalities. Z.S. Med. Features include billing, scheduling, waitlists, insights, teletherapy, documentation, and more to streamline your clinic! We only considered studies published in English, which may have led to leaving out some studies published in other languages. Creating value in health care through big data: Opportunities and policy implications. Knowledge Base: How to distinguish ROS from Exam. 15, 869877. The fusion AI model has two componentsan on-chip AI model that continuously analyzes patient electrocardiogram (ECG) data and a cloud AI model that combines EMR and prediction scores from. In contrast to early fusion, the loss from the final model is propagated back to the feature extraction model during training so that the learned feature representations are improved through iterative updating of the feature weights. reviewed and verified the extracted data. As the causes of many diseases are complex, many factors, including inherited genetics, lifestyle, and living environments, contribute to the development of diseases. Since our review shows that the fusion of medical imaging and clinical context data can improve the performance of AI models, we recommend attempting fusion approaches when multimodal data is obtainable. They early fused imaging features with the cognitive test scores through concatenation before feeding them into the fully connected network. In many applications of medicine, the integration (fusion) of different data sources has become necessary for effective prediction, diagnosis, treatment, and planning decisions by combining the complementary power of different modalities, thereby bringing us closer to the goal of precision medicine2,3. Furthermore, not all articles provided confidence bounds, making it difficult to compare their results statistically. It can also connect with laboratories and imaging centers to instantly collect, monitor, and share lab and imaging results. They concatenated the features of both modalities and fed them into a linear and quadratic discriminant analysis algorithm for diagnosis. Control 68, 102729 (2021). Specifically, most of the studies that focused on detecting neurological diseases were for AD (\(n=4\))13,14,15,49, and MCI (\(n = 4\))37,42,50,51. Fusing heterogeneous data for Alzheimers disease classification. Late fusion: It trains separate ML models on data of each modality, and the final decision leverages the predictions of each model26. 8, 128136 (2019). Transl. Ouzzani, M., Hammady, H., Fedorowicz, Z. The most important question when using multimodal data is how to fuse thema field of growing interest among researchers. The Lancet 390, 21832193. https://doi.org/10.1016/j.nic.2005.09.008 (2005). If your provider has already granted you access, follow these steps to set up your account. As a result, the fusion model outperformed the individual models. In this scoping review, we followed the guidelines recommended by the PRISMA-ScR28. Two authors (F.M. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. We consider such data as a single modality, i.e., the EHR modality or imaging modality. Qiu, S. et al. arXiv preprint arXiv:2111.04898 (2021). A primary interest of our review is to identify the fusion strategies that the included studies used to improve the performance of ML models for different clinical outcomes. Comfere, N. I. et al. Fang et al.38 developed a prediction system by jointly fusing CT scans and clinical data to predict the progression of COVID-19 malignancy. Rep. 10, 19 (2020). Also, we excluded studies that used different types of data from the same modality, such as studies that only combined two or more imaging types (e.g. The most prominent dataset was the ADNI, containing MRI and PET images collected from about 1700 individuals in addition to clinical and genetic information. used CNN to extract image features and then concatenated them directly with the clinical data to feed into a SoftMax classifier. A comprehensive survey on multimodal medical signals fusion for smart healthcare systems. Our smart, flexible content makes documentation fast and convenient. Open Access funding provided by Qatar National Library. In the literature, some other reviews have been published on the use of AI for multimodal medical data fusion20,21,22,23,24,25,26; however, they differ from our review in terms of their scope and coverage. Then, we present the data fusion strategies that we use to investigate the studies from the perspective of multimodal fusion. Modality fusion strategies play a significant role in these studies. Joint fusion was used for diagnostic purposes in 5 studies19,49,53,55,56. Because it's Web-based, Practice Fusion has a cost edge on on-premises medical-record solutions. Article 33, 11151122 (2014). 15, 753033 (2021). A patients medical chart may contain different note types, documenting office or telemedicine visits (encounters) and patient calls, such as: Depending on the type of ambulatory practice whether a solo practitioner or a member of a medical group that includes multiple practicesa patients chart may contain notes from one provider or from multiple providers who have seen the patient. Hsu, M.-Y. Fransen, P. S. et al. Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. This emphasizes the utility and significance of multimodal fusion approaches in clinical applications. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective. Medications, diagnoses and more. We also extracted information on the diseases for which fusion methods were implemented. To download their health record, patients and their authorized representatives must first log into their Patient Fusion Account. Out of the 22 early fusion studies, 19 studies12,13,15,25,33,34,35,36,39,41,42,43,44,45,50,51,52,53 used manual or software-based imaging features, and 3 studies used neural network-based architectures to extract imaging features before combining with other clinical data modality16,18,54. To obtain Interpretation on deep multimodal fusion for diagnostic classification. Neurosci. Res. Furthermore, the reader will develop an understanding of how ML models could be designed to align data from different modalities for various clinical tasks. What is Fusion? Through this scoping review, we offer new insights for researchers interested in knowing the current state of knowledge within this research field. Richer fusion network for breast cancer classification based on multimodal data. Glaucoma. Fusion strategies associated with clinical outcomes for different diseases. They evaluated the performance against single modality models and observed that the results for multimodal fusion were better. We propose a multi-modal data fusion method of ultrasonic images and electronic medical records named M-US-EMRs model to automatically screen CHD. Practice Fusion provides templates that can be customized to match the workflow of a practice, and it is designed to support a variety of specialties such as cardiology, dermatology, physical therapy, mental health, internal medicine and many more. All authors read and approved the final manuscript. Internet Explorer). Akramifard, H., Balafar, M. A., Razavi, S. N. & Ramli, A. R. Early detection of Alzheimers disease based on clinical trials, three-dimensional imaging data, and personal information using autoencoders. A typology of reviews: An analysis of 14 review types and associated methodologies. In Proceedings of the European Conference on Computer Vision (ECCV), 435453 (2018).

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