Deep learning for brain disorders: from data processing to disease treatment

Burgos, Ninon; Bottani, Simona; Faouzi, Johann; Thibeau–Sutre, Elina; Colliot, Olivier

doi:10.1093/bib/bbaa310

Cited by 18 publications

(10 citation statements)

References 239 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compared three 3D U-net models differentiated by the addition of residual modules, of attention modules or of transformer layers, used as simple generators and also within a conditional GAN setting with the addition of a patch-based discriminator. These models have widely been used for the image translation of medical images [ 71 , 72 ], but to the best of our knowledge, their application to clinical data has not been proven yet. The proposed models were trained using 230 image pairs and tested on two different test sets: 26 image pairs had both a T1nce and T1ce of good or medium quality and 51 image pairs had a T1nce of good or medium quality and a T1ce of bad quality.…”

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

Bottani,

Thibeau-Sutre,

Maire

et al. 2024

BMC Med Imaging

Self Cite

View full text Add to dashboard Cite

Background Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse. Methods We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area. Results Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images. Conclusion We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.

show abstract

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

Bottani,

Thibeau-Sutre,

Maire

et al. 2024

BMC Med Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Deep-learning algorithms are an active area of research in medical image processing. These algorithms can extract information from conventional MR images, including features that cannot be recognized by the human eye, and help to make a more accurate diagnosis ( 34 ), prognosis evaluation ( 35 ) and therapeutic guidance ( 36 ). Our proposed multimodal MIL-CoaT deep-learning network can perform multicategory classification using hybrid MRI sequences.…”

Section: Discussionmentioning

confidence: 99%

Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging

Huang

Chen²,

Liu³

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

BackgroundDifferential diagnosis of demyelinating diseases of the central nervous system is a challenging task that is prone to errors and inconsistent reading, requiring expertise and additional examination approaches. Advancements in deep-learning-based image interpretations allow for prompt and automated analyses of conventional magnetic resonance imaging (MRI), which can be utilized in classifying multi-sequence MRI, and thus may help in subsequent treatment referral.MethodsImaging and clinical data from 290 patients diagnosed with demyelinating diseases from August 2013 to October 2021 were included for analysis, including 67 patients with multiple sclerosis (MS), 162 patients with aquaporin 4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD), and 61 patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model. This novel transformer-based deep-learning model architecture was designed to be versatile in handling with multiple image sequences (coronal T2-weighted and sagittal T2-fluid attenuation inversion recovery) and scanning locations (brain and spinal cord) for differentiating among MS, NMOSD, and MOGAD. Model performances were evaluated using the area under the receiver operating curve (AUC) and the confusion matrices measurements. The classification accuracy between the fusion model and the neuroradiological raters was also compared.ResultsThe fusion model that was trained with combined brain and spinal cord MRI achieved an overall improved performance, with the AUC of 0.933 (95%CI: 0.848, 0.991), 0.942 (95%CI: 0.879, 0.987) and 0.803 (95%CI: 0.629, 0.949) for MS, AQP4+ NMOSD, and MOGAD, respectively. This exceeded the performance using the brain or spinal cord MRI alone for the identification of the AQP4+ NMOSD (AUC of 0.940, brain only and 0.689, spinal cord only) and MOGAD (0.782, brain only and 0.714, spinal cord only). In the multi-category classification, the fusion model had an accuracy of 81.4%, which was significantly higher compared to rater 1 (64.4%, p=0.04<0.05) and comparable to rater 2 (74.6%, p=0.388).ConclusionThe proposed novel transformer-based model showed desirable performance in the differentiation of MS, AQP4+ NMOSD, and MOGAD on brain and spinal cord MRI, which is comparable to that of neuroradiologists. Our model is thus applicable for interpretating conventional MRI in the differential diagnosis of demyelinating diseases with overlapping lesions.

show abstract

“…Other reviews do not present the breakthroughs of the last 4-5 years (attention mechanism, transformers, BERTmodels, etc. ), either because they are relatively old (11)(12)(13), or because they present the current state-of-the-art at a high level (14)(15)(16)(17). Lastly, Wu et al (18) present a solid review, which is focused on DL approaches only.…”

Section: Related Workmentioning

confidence: 99%

Natural language processing in clinical neuroscience and psychiatry: A review

et al. 2022

View full text Add to dashboard Cite

Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.

show abstract

Deep learning for brain disorders: from data processing to disease treatment

Cited by 18 publications

References 239 publications

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging

Natural language processing in clinical neuroscience and psychiatry: A review

Contact Info

Product

Resources

About