Deep learning in rare disease. Detection of tubers in tuberous sclerosis complex

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning

BACKGROUND AND PURPOSE

Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy.

MATERIALS AND METHODS

We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model.

RESULTS

The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods.

CONCLUSIONS

The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.

Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment

PURPOSES

To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines.

MATERIALS AND METHODS

A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients.

RESULTS

Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%-73%) CLAIM items reported. Adherence has improved over time based on publication year.

CONCLUSION

The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.

RDDL: A systematic ensemble pipeline tool that streamlines balancing training schemes to reduce the effects of data imbalance in rare-disease-related deep-learning applications

Identifying lowly prevalent diseases, or rare diseases, in their early stages is key to disease treatment in the medical field. Deep learning techniques now provide promising tools for this purpose. Nevertheless, the low prevalence of rare diseases entangles the proper application of deep networks for disease identification due to the severe class-imbalance issue. In the past decades, some balancing methods have been studied to handle the data-imbalance issue. The bad news is that it is verified that none of these methods guarantees superior performance to others. This performance variation causes the need to formulate a systematic pipeline with a comprehensive software tool for enhancing deep-learning applications in rare disease identification. We reviewed the existing balancing schemes and summarized a systematic deep ensemble pipeline with a constructed tool called RDDL for handling the data imbalance issue. Through two real case studies, we showed that rare disease identification could be boosted with this systematic RDDL pipeline tool by lessening the data imbalance problem during model training. The RDDL pipeline tool is available at https://github.com/cobisLab/RDDL/.

Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network

Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.

Machine learning in rare disease

High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO

Congenital Generalized Lipodystrophy (CGL) is a rare autosomal recessive disease characterized by near complete absence of functional adipose tissue from birth. CGL diagnosis can be based on clinical data including acromegaloid features, acanthosis nigricans, reduction of total body fat, muscular hypertrophy, and protrusion of the umbilical scar. The identification and knowledge of CGL by the health care professionals is crucial once it is associated with severe and precocious cardiometabolic complications and poor outcome. Image processing by deep learning algorithms have been implemented in medicine and the application into routine clinical practice is feasible. Therefore, the aim of this study was to identify congenital generalized lipodystrophy phenotype using deep learning. A deep learning approach model using convolutional neural network was presented as a detailed experiment with evaluation steps undertaken to test the effectiveness. These experiments were based on CGL patient's photography database. The dataset consists of two main categories (training and testing) and three subcategories containing photos of patients with CGL, individuals with malnutrition and eutrophic individuals with athletic build. A total of 337 images of individuals of different ages, children and adults were carefully chosen from internet open access database and photographic records of stored images of medical records of a reference center for inherited lipodystrophies. For validation, the dataset was partitioned into four parts, keeping the same proportion of the three subcategories in each part. The fourfold cross-validation technique was applied, using 75% (3 parts) of the data as training and 25% (1 part) as a test. Following the technique, four tests were performed, changing the parts that were used as training and testing until each part was used exactly once as validation data. As a result, a mean accuracy, sensitivity, and specificity were obtained with values of [90.85 ± 2.20%], [90.63 ± 3.53%] and [91.41 ± 1.10%], respectively. In conclusion, this study presented for the first time a deep learning model able to identify congenital generalized lipodystrophy phenotype with excellent accuracy, sensitivity and specificity, possibly being a strategic tool for detecting this disease.

eTSC-Net: A Parameter-efficient Convolutional Neural Network for Drug Treatment Outcome Studies of Pediatric Epilepsy.. [DOI: 10.21203/rs.3.rs-2024294/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background: Ability to predict the outcomes of pharmacological treatment of epilepsy in pediatric patients with tuberous sclerosis complex (TSC) can confer a distinct leverage and guide therapeutic decision-making. Multi-contrast magnetic resonance imaging (MRI) is routinely used for diagnosis of TSC by pediatricians. We propose a parameter-efficient convolutional neural network with multi-contrast images to predict the drug treatment outcomes of pediatric epilepsy in TSC. Methods: Image-based models were generated using the EfficientNet3D-B0 network architecture. A weighted average ensemble network with multi-contrast images was created as the final model. The proposed neural network is named as Efficient Tuberous sclerosis complex-Net (eTSC-Net).We compared our methods with a Residual Network 3D（ResNet3D) model. We trained a 3D-ResNet on our T2FLAIR data. Binary classification models were trained to distinguish non-controlled group patients from controlled group patients on T2W and T2FLAIR images. We trained all the models using an Nvidia RTX A6000 Graphical Processing Unit (GPU) card. Area under curve(AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to assess the classification performance for each model in each cohort. The differences between subgroups were assessed using independent samples t test and pvalues < 0.05 were considered indicative of statistical significance. Results: The proposed neural network (eTSC-Net) achieved the best performance with an AUC value of 0.833 and 90.0% accuracy in the testing cohort, which was better than other models. Conclusions: The results demonstrated the ability of the proposed method for predicting drug treatment outcomes in pediatric TSC-related epilepsy. eTSC-Net can serve as a useful computer-aided diagnostic tool to help clinical radiologists formulate more targeted treatment.

Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm

BACKGROUND

Intraoperative neurophysiological monitoring is essential in neurosurgical procedures. In this study, we built and evaluated the performance of a deep neural network in differentiating between the presence and absence of a lateral spread response, which provides critical information during microvascular decompression surgery for the treatment of hemifacial spasm using intraoperatively acquired electromyography images.

METHODS AND FINDINGS

A total of 3,674 image screenshots of monitoring devices from 50 patients were prepared, preprocessed, and then adopted into training and validation sets. A deep neural network was constructed using current-standard, off-the-shelf tools. The neural network correctly differentiated 50 test images (accuracy, 100%; area under the curve, 0.96) collected from 25 patients whose data were never exposed to the neural network during training or validation. The accuracy of the network was equivalent to that of the neuromonitoring technologists (p = 0.3013) and higher than that of neurosurgeons experienced in hemifacial spasm (p < 0.0001). Heatmaps obtained to highlight the key region of interest achieved a level similar to that of trained human professionals. Provisional clinical application showed that the neural network was preferable as an auxiliary tool.

CONCLUSIONS

A deep neural network trained on a dataset of intraoperatively collected electromyography data could classify the presence and absence of the lateral spread response with equivalent performance to human professionals. Well-designated applications based upon the neural network may provide useful auxiliary tools for surgical teams during operations.

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.

Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization

Primary malignancies in adult brains are globally fatal. Computer vision, especially recent developments in artificial intelligence (AI), have created opportunities to automatically characterize and diagnose tumor lesions in the brain. AI approaches have provided scores of unprecedented accuracy in different image analysis tasks, including differentiating tumor-containing brains from healthy brains. AI models, however, perform as a black box, concealing the rational interpretations that are an essential step towards translating AI imaging tools into clinical routine. An explainable AI approach aims to visualize the high-level features of trained models or integrate into the training process. This study aims to evaluate the performance of selected deep-learning algorithms on localizing tumor lesions and distinguishing the lesion from healthy regions in magnetic resonance imaging contrasts. Despite a significant correlation between classification and lesion localization accuracy (R = 0.46, p = 0.005), the known AI algorithms, examined in this study, classify some tumor brains based on other non-relevant features. The results suggest that explainable AI approaches can develop an intuition for model interpretability and may play an important role in the performance evaluation of deep learning models. Developing explainable AI approaches will be an essential tool to improve human–machine interactions and assist in the selection of optimal training methods.

Deep learning-based facial image analysis in medical research: a systematic review protocol

INTRODUCTION

Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients' welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis.

METHODS

Databases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study.

ETHICS AND DISSEMINATION

As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020196473.

Epilepsy Is Heterogeneous in Early-Life Tuberous Sclerosis Complex

BACKGROUND

Epilepsy in tuberous sclerosis complex (TSC) typically presents with early onset, multiple seizure types, and intractability. However, variability is observed among individuals. Here, detailed individual data on seizure characteristics collected prospectively during early life were used to define epilepsy profiles in this population.

METHODS

Children aged zero to 36 months were followed longitudinally. Caregivers kept daily seizure diaries, including onset and daily counts for each seizure type. Patients with >70% seizure diary completion and >365 diary days were included. Developmental outcomes at 36 months were compared between subgroups.

RESULTS

Epilepsy was seen in 124 of 156 (79%) participants. Seizure onset occurred from zero to 29.5 months; 93% had onset before age 12 months. Focal seizures and epileptic spasms were most common. Number of seizures (for median 897 days) ranged from 1 to 9128. Hierarchical clustering based on six metrics of seizure burden (age of onset, total seizures, ratio of seizure days to nonseizure days, seizures per seizure day, and worst seven- and 30-day stretches) revealed two distinct groups with broadly favorable and unfavorable epilepsy profiles. Subpopulations within each group showed clinically meaningful differences in seizure burden. Groups with higher seizure burden had worse developmental outcomes at 36 months.

CONCLUSIONS

Although epilepsy is highly prevalent in TSC, not all young children with TSC have the same epilepsy profile. At least two phenotypic subpopulations are discernible based on seizure burden. Early and aggressive treatments for epilepsy in TSC may be best leveraged by targeting specific subgroups based on phenotype severity.

Convolutional neural networks to identify malformations of cortical development: A feasibility study

OBJECTIVE

To develop and test a deep learning model to automatically detect malformations of cortical development (MCD).

METHODS

We trained a deep learning model to distinguish between diffuse cortical malformation (CM), periventricular nodular heterotopia (PVNH), and normal magnetic resonance imaging (MRI). We trained 4 different convolutional neural network (CNN) architectures. We used batch normalization, global average pooling, dropout layers, transfer learning, and data augmentation to minimize overfitting.

RESULTS

There were 45 subjects (866 images) with a normal MRI, 52 subjects (790 images) with CM, and 32 subjects (750 images) with PVNH. There was no subject overlap between the training, validation, and test sets. The InceptionResNetV2 architecture performed best in the validation set in all models and was evaluated in the test set with the following results: 1) the model distinguishing between CM and normal MRI yielded an area under the curve (AUC) of 0.89 and accuracy of 0.81; 2) the model distinguishing between PVNH and normal MRI yielded an AUC of 0.90 and accuracy of 0.84; 3) the model distinguishing between the three classes (CM, PVNH, and normal MRI) yielded an AUC of 0.88 and accuracy of 0.74. Visualization with gradient-weighted class activation maps and saliency maps showed that the deep learning models classified images based on relevant areas within each image.

SIGNIFICANCE

This study showed that CNNs can detect MCD at a clinically useful performance level with a fully automated workflow without image feature selection.

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

[Application of deep learning assisted electronic laryngoscope in diagnosis of laryngeal leukoplakia]

In recent years, medical imaging technology and computer technology have made great progress. On the one hand, with the development and popularization of electronic laryngoscope, the image of electronic laryngoscope plays a very important role in the diagnosis of vocal cord lesions. On the other hand, deep learning algorithm，especially convolutional neural networkhas gradually become the first choice of medical image recognition since the foundation of deep learning algorithm. So far, deep learning algorithm has made great contributions in many disciplines. In this paper, the basic concept of deep learning, the current status of image recognition of vocal cord lesions, and the prospect of research based on deep learning in vocal cord image lesions recognition are reviewed.