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General General

Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.

In Annals of the rheumatic diseases ; h5-index 121.0

Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals' input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person's Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved.

Shoop-Worrall Stephanie J W, Cresswell Katherine, Bolger Imogen, Dillon Beth, Hyrich Kimme L, Geifman Nophar

2021-Jul-05

arthritis, epidemiology, health care, juvenile, outcome and process assessment, outcome assessment, patient reported outcome measures

Pathology Pathology

A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens.

In EBioMedicine

BACKGROUND : The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time.

METHODS : Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data.

FINDINGS : The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922-0.997) (86/88) sensitivity and 100% (95% CI = 0.958-1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method.

INTERPRETATION : Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures.

Renuse Santosh, Vanderboom Patrick M, Maus Anthony D, Kemp Jennifer V, Gurtner Kari M, Madugundu Anil K, Chavan Sandip, Peterson Jane A, Madden Benjamin J, Mangalaparthi Kiran K, Mun Dong-Gi, Singh Smrita, Kipp Benjamin R, Dasari Surendra, Singh Ravinder J, Grebe Stefan K, Pandey Akhilesh

2021-Jul-03

COVID-19, Diagnostic assays, Ion mobility, Machine learning, Mass spectrometry, SARS-CoV-2

General General

Quantified assessment of deep brain stimulation on Parkinson's patients with task fNIRS measurements and functional connectivity analysis: a pilot study.

In Chinese neurosurgical journal

BACKGROUND : Deep brain stimulation (DBS) has proved effective for Parkinson's disease (PD), but the identification of stimulation parameters relies on doctors' subjective judgment on patient behavior.

METHODS : Five PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (△HbO2) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities.

RESULTS : We discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors.

CONCLUSIONS : Task fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.

TRIAL REGISTRATION : Name: Accurate treatment under the multidisciplinary cooperative diagnosis and treatment model of Parkinson's disease. Registration number is ChiCTR1900022715. Date of registration is April 23, 2019.

Yu Ningbo, Liang Siquan, Lu Jiewei, Shu Zhilin, Li Haitao, Yu Yang, Wu Jialing, Han Jianda

2021-Jul-05

Brain efficiency, Deep brain stimulation programming, Functional connectivity, Parkinson’s disease

General General

Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection.

In Computers in biology and medicine

The COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Although vaccination has started, it will take time to reach everyone, especially in developing nations, and computer scientists are striving to come up with competent methods using image analysis. In this work, a classifier ensemble technique is proposed, utilizing Choquet fuzzy integral, wherein convolutional neural network (CNN) based models are used as base classifiers. It classifies chest X-ray images from patients with common Pneumonia, confirmed COVID-19, and healthy lungs. Since there are few samples of COVID-19 cases for training on a standard CNN model from scratch, we use the transfer learning scheme to train the base classifiers, which are InceptionV3, DenseNet121, and VGG19. We utilize the pre-trained CNN models to extract features and classify the chest X-ray images using two dense layers and one softmax layer. After that, we combine the prediction scores of the data from individual models using Choquet fuzzy integral to get the final predicted labels, which is more accurate than the prediction by the individual models. To determine the fuzzy-membership values of each classifier for the application of Choquet fuzzy integral, we use the validation accuracy of each classifier. The proposed method is evaluated on chest X-ray images in publicly available repositories (IEEE and Kaggle datasets). It provides 99.00%, 99.00%, 99.00%, and 99.02% average recall, precision, F-score, and accuracy, respectively. We have also evaluated the performance of the proposed model on an inter-dataset experimental setup, where chest X-ray images from another dataset (CMSC-678-ML-Project GitHub dataset) are fed to our trained model and we have achieved 99.05% test accuracy on this dataset. The results are better than commonly used classifier ensemble methods as well as many state-of-the-art methods.

Dey Subhrajit, Bhattacharya Rajdeep, Malakar Samir, Mirjalili Seyedali, Sarkar Ram

2021-Jun-22

COVID-19, Choquet integral, Deep learning, Ensemble method, Machine learning, Transfer learning, X-ray image

Public Health Public Health

Associations of reproductive breast cancer risk factors with breast tissue composition.

In Breast cancer research : BCR

BACKGROUND : We investigated the associations of reproductive factors with the percentage of epithelium, stroma, and fat tissue in benign breast biopsy samples.

METHODS : This study included 983 cancer-free women with biopsy-confirmed benign breast disease (BBD) within the Nurses' Health Study and Nurses' Health Study II cohorts. The percentage of each tissue type (epithelium, stroma, and fat) was measured on whole-section images with a deep-learning technique. All tissue measures were log-transformed in all the analyses to improve normality. The data on reproductive variables and other breast cancer risk factors were obtained from biennial questionnaires. Generalized linear regression was used to examine the associations of reproductive factors with the percentage of tissue types, while adjusting for known breast cancer risk factors.

RESULTS : As compared to parous women, nulliparous women had a smaller percentage of epithelium (β = - 0.26, 95% confidence interval [CI] - 0.41, - 0.11) and fat (β = - 0.34, 95% CI - 0.54, - 0.13) and a greater percentage of stroma (β = 0.04, 95% CI 0.01, 0.08). Among parous women, the number of children was inversely associated with the percentage of stroma (β per child = - 0.01, 95% CI - 0.02, - 0.00). The duration of breastfeeding of ≥ 24 months was associated with a reduced proportion of fat (β = - 0.30, 95% CI - 0.54, - 0.06; p-trend = 0.04). In a separate analysis restricted to premenopausal women, older age at first birth was associated with a greater proportion of epithelium and a smaller proportion of stroma.

CONCLUSIONS : Our findings suggest that being nulliparous as well as having a fewer number of children (both positively associated with breast cancer risk) is associated with a smaller proportion of epithelium and a greater proportion of stroma, potentially suggesting the importance of epithelial-stromal interactions. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.

Yaghjyan Lusine, Austin-Datta Rebecca J, Oh Hannah, Heng Yujing J, Vellal Adithya D, Sirinukunwattana Korsuk, Baker Gabrielle M, Collins Laura C, Murthy Divya, Rosner Bernard, Tamimi Rulla M

2021-Jul-05

Age at first child, Benign breast disease, Breast cancer risk, Breastfeeding, Parity

General General

X-Ray and CT-Scan-Based Automated Detection and Classification of Covid-19 Using Convolutional Neural Networks (CNN).

In Biomedical signal processing and control

Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people. While this virus has a low fatality rate, the problem is that it is highly infectious, and as a result, it has infected a large number of people, putting a strain on the healthcare system, hence, Covid-19 identification in patients has become critical. The goal of this research is to use X-rays images and computed tomography(CT) images to introduce a deep learning strategy based on the Convolutional Neural Network (CNN) to automatically detect and identify the Covid-19 disease. We have implemented two different classifications using CNN(binary classification and multiclass classification). A total of 3,877 image datasets from CTs and X-rays were utilised to train the model in binary classification, with 1,917 images from Covid-19 infected individuals among them. The experimental results for binary classification show an overall accuracy of 99.64%, recall(or sensitivity) of 99.58%, the precision of 99.56%, F1-score of 99.59%, and ROC of 100%. For multiple classifications, the model was trained using a total of 6,077 images, with 1,917 images of Covid-19 infected people, 1,960 images of normal healthy people, and 2,200 images of pneumonia infected people. The experimental results for multiple classifications show an accuracy of 98.28%, recall(or sensitivity) of 98.25%, the precision of 98.22%, F1-score of 98.23%, and ROC of 99.87%. On the currently available dataset, the model produced the desired results, and it can assist healthcare workers in quickly detecting Covid-19 positive patients.

Thakur Samritika, Kumar Aman

2021-Jun-30