A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification

Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data, as it was done to generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians to diagnose the underlying diseases present in a limited number of uploaded recordings. However, this approach suffers from potential mislabeling of the coughs, as well as notable disagreement between experts. In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification. First, we leverage existing SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classification models. The consistency of the re-labeled data is demonstrated in that it exhibits a high degree of class separability, 3x higher than that of the user-labeled data, despite the expert label inconsistency present in the original dataset. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs, which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a cough classifier. This SSL approach can be used to combine the medical knowledge of several experts to improve the database consistency for any diagnostic classification task

Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.Comment: 28 pages, 11 figures, 12 table

Supplementary information dsRNAi-mediated silencing of PIAS2beta specifically kills anaplastic carcinomas by mitotic catastrophe

Supplementary information index: -Supplementary Figures 1-10 -Supplementary Figure 11-Graphical Abstract -Unprocessed Scans of westerns from Supplementary FiguresThe E3 SUMO ligase PIAS2 is expressed at high levels in differentiated papillary thyroid carcinomas but at low levels in anaplastic thyroid carcinomas (ATC), an undifferentiated cancer with high mortality. We show here that depletion of the PIAS2 beta isoform with a transcribed double-stranded RNA-directed RNA interference (PIAS2b-dsRNAi) specifically inhibits growth of ATC cell lines and patient primary cultures in vitro and of orthotopic patient-derived xenografts (oPDX) in vivo. Critically, PIAS2b-dsRNAi does not affect growth of normal or non-anaplastic thyroid tumor cultures (differentiated carcinoma, benign lesions) or cell lines. PIAS2b-dsRNAi also has an anti-cancer effect on other anaplastic human cancers (pancreas, lung, and gastric). Mechanistically, PIAS2b is required for proper mitotic spindle and centrosome assembly, and it is a dosage-sensitive protein in ATC. PIAS2b depletion promotes mitotic catastrophe at prophase. High-throughput proteomics reveals the proteasome (PSMC5) and spindle cytoskeleton (TUBB3) to be direct targets of PIAS2b SUMOylation at mitotic initiation. These results identify PIAS2b-dsRNAi as a promising therapy for ATC and other aggressive anaplastic carcinomas.Supplementary information Reporting Summary Description of Additional Supplementary Files Peer Review File Supplementary Movie 1 Supplementary Movie 2 Supplementary Movie 3 Supplementary Dataset 1 Supplementary Dataset 2 Supplementary Dataset 3 Supplementary Dataset 4 Source dataPeer reviewe

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Travel burden and clinical presentation of retinoblastoma: analysis of 1024 patients from 43 African countries and 518 patients from 40 European countries

BACKGROUND: The travel distance from home to a treatment centre, which may impact the stage at diagnosis, has not been investigated for retinoblastoma, the most common childhood eye cancer. We aimed to investigate the travel burden and its impact on clinical presentation in a large sample of patients with retinoblastoma from Africa and Europe. METHODS: A cross-sectional analysis including 518 treatment-naïve patients with retinoblastoma residing in 40 European countries and 1024 treatment-naïve patients with retinoblastoma residing in 43 African countries. RESULTS: Capture rate was 42.2% of expected patients from Africa and 108.8% from Europe. African patients were older (95% CI -12.4 to -5.4, p<0.001), had fewer cases of familial retinoblastoma (95% CI 2.0 to 5.3, p<0.001) and presented with more advanced disease (95% CI 6.0 to 9.8, p<0.001); 43.4% and 15.4% of Africans had extraocular retinoblastoma and distant metastasis at the time of diagnosis, respectively, compared to 2.9% and 1.0% of the Europeans. To reach a retinoblastoma centre, European patients travelled 421.8 km compared to Africans who travelled 185.7 km (p<0.001). On regression analysis, lower-national income level, African residence and older age (p<0.001), but not travel distance (p=0.19), were risk factors for advanced disease. CONCLUSIONS: Fewer than half the expected number of patients with retinoblastoma presented to African referral centres in 2017, suggesting poor awareness or other barriers to access. Despite the relatively shorter distance travelled by African patients, they presented with later-stage disease. Health education about retinoblastoma is needed for carers and health workers in Africa in order to increase capture rate and promote early referral

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

In-depth assessment of potential new data integration into the MIP

In the scope of the Human Brain Project (SGA2), this document summarizes from the technical perspective the main challenges faced by the current Medical Informatics Platform for the integration of three new types of data: complex neuroimaging, omics, and data from wearable devices. Details are discussed up to the implementation level, considering risks, costs, and possible ethical and data privacy issues. The specific recommendations for the future development of the MIP are collected in the accompanying document “Recommendations for the MIP technical development during SGA3”, with component ID C2976

Recommendations for the MIP Technical Development During SGA3

In the scope of the Human Brain Project (SGA2), this document collects the specific technical recommendations for the future development of the MIP derived from the potential integration of new types of data into the platform. This integration is discussed in detail in the accompanying document “In-depth assessment of potential new data integration into the MIP”, with component ID C2975. Here, we begin with a high-level description of the MIP architecture and of its different software components, and then we analyse the evolution of each of them from a technical perspective

Knowledge, Machine Learning and Atrial Fibrillation: More Ingredients for a Tastier Cocktail

Fifty years after the publication of the first algorithms for the automatic detection of Atrial Fibrillation (AF), this cardiac condition is still the most studied from the computer science and engineering perspectives. Machine learning techniques are widely applied to a variety of problems, including detection, characterization, prediction and simulation, in general with promising results. In the last years, the Big Data + Deep Learning binomial is getting most of the attention in academia and industry, but on many occasions this approach fails on capitalizing all the knowledge acquired in previous decades of research. This article, written as a companion to the keynote with the same title presented in the CinC 2020 conference, tries to illustrate the importance of exploiting expert knowledge and classical approaches in synergy with the most advanced deep learning methods, which by themselves have fundamental limitations. The discussion is built around the AF detection problem and the conclusions extracted from the Physionet/CinC Challenge 2017, but the main points can be relevant in other problems for which humans have a better answer than computers, and this answer can be described

A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification

Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data, as it was done to generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians to diagnose the underlying diseases present in a limited number of uploaded recordings. However, this approach suffers from potential mislabeling of the coughs, as well as notable disagreement between experts. In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification. First, we leverage existing SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classification models. The consistency of the re-labeled data is demonstrated in that it exhibits a high degree of class separability, 3x higher than that of the user-labeled data, despite the expert label inconsistency present in the original dataset. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs, which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a cough classifier. This SSL approach can be used to combine the medical knowledge of several experts to improve the database consistency for any diagnostic classification task.ES