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Ophthalmology Ophthalmology

Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement.

In Indian journal of ophthalmology

Diabetic retinopathy (DR) is an emerging preventable cause of blindness in India. All India Ophthalmology Society (AIOS) and Vitreo-Retinal Society of India (VRSI) have initiated several measures to improve screening of DR screening in India. This article is a consensus statement of the AIOS DR task force and VRSI on practical guidelines of DR screening in India. Although there are regional variations in the prevalence of diabetes in India at present, all the States in India should screen their population for diabetes and its complications. The purpose of DR screening is to identify people with sight-threatening DR (STDR) so that they are treated promptly to prevent blindness. This statement provides strategies for the identification of people with diabetes for DR screening, recommends screening intervals in people with diabetes with and without DR, and describes screening models that are feasible in India. The logistics of DR screening emphasizes the need for dynamic referral pathways with feedback mechanisms. It provides the clinical standards required for DR screening and treatment of STDR and addresses the governance and quality assurance (QA) standards for DR screening in Indian settings. Other aspects incorporate education and training, recommendations on Information technology (IT) infrastructure, potential use of artificial intelligence for grading, data capture, and requirements for maintenance of a DR registry. Finally, the recommendations include public awareness and the need to work with diabetologists to control the risk factors so as to have a long-term impact on prevention of diabetes blindness in India.

Raman Rajiv, Ramasamy Kim, Rajalakshmi Ramachandran, Sivaprasad Sobha, Natarajan S

2020-Nov-26

Consensus, India, diabetic retinopathy screening, guidelines

General General

Predicting Body Composition From Anthropometrics.

In Journal of diabetes science and technology ; h5-index 38.0

Body weight, height, and other simple, noninvasive anthropometric measures are the cornerstones of epidemiological research. Body composition determinants such as fat and lean tissue masses and their distributions are better associated with metabolic conditions, such as diabetes, than anthropometrics alone. However, body composition is generally more challenging to measure. This analysis article comments on the manuscript by Cichosz et al that appeared in this issue of the Journal of Diabetes Science and Technology, where a machine-learning approach was developed to predict body composition using measured anthropometric parameters for potentially easier estimations of risk factors of metabolic diseases in the future.

Chen Kong Y

2020-Dec-03

fat distribution, fat mass, lean body mass, machine learning, metabolic risks

Surgery Surgery

Applying the electronic nose for pre-operative SARS-CoV-2 screening.

In Surgical endoscopy ; h5-index 65.0

BACKGROUND : Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis.

METHODS : Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability.

RESULTS : 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96.

CONCLUSIONS : The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Wintjens Anne G W E, Hintzen Kim F H, Engelen Sanne M E, Lubbers Tim, Savelkoul Paul H M, Wesseling Geertjan, van der Palen Job A M, Bouvy Nicole D

2020-Dec-02

COVID-19, Electronic nose, Exhaled air, Innovative diagnostics, Volatile organic compounds

General General

Bacterial Immunogenicity Prediction by Machine Learning Methods.

In Vaccines

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

Dimitrov Ivan, Zaharieva Nevena, Doytchinova Irini

2020-Nov-30

immunogenicity prediction, machine learning, protective immunogens

Surgery Surgery

Applying the electronic nose for pre-operative SARS-CoV-2 screening.

In Surgical endoscopy ; h5-index 65.0

BACKGROUND : Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis.

METHODS : Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability.

RESULTS : 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96.

CONCLUSIONS : The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

Wintjens Anne G W E, Hintzen Kim F H, Engelen Sanne M E, Lubbers Tim, Savelkoul Paul H M, Wesseling Geertjan, van der Palen Job A M, Bouvy Nicole D

2020-Dec-02

COVID-19, Electronic nose, Exhaled air, Innovative diagnostics, Volatile organic compounds

Radiology Radiology

Statistical inference of the inter-sample Dice distribution for discriminative CNN brain lesion segmentation models

ArXiv Preprint

Discriminative convolutional neural networks (CNNs), for which a voxel-wise conditional Multinoulli distribution is assumed, have performed well in many brain lesion segmentation tasks. For a trained discriminative CNN to be used in clinical practice, the patient's radiological features are inputted into the model, in which case a conditional distribution of segmentations is produced. Capturing the uncertainty of the predictions can be useful in deciding whether to abandon a model, or choose amongst competing models. In practice, however, we never know the ground truth segmentation, and therefore can never know the true model variance. In this work, segmentation sampling on discriminative CNNs is used to assess a trained model's robustness by analyzing the inter-sample dice distribution on a new patient solely based on their magnetic resonance (MR) images. Furthermore, by demonstrating the inter-sample Dice observations are independent and identically distributed with a finite mean and variance under certain conditions, a rigorous confidence based decision rule is proposed to decide whether to reject or accept a CNN model for a particular patient. Applied to the ISLES 2015 (SISS) dataset, the model identified 7 predictions as non-robust, and the average Dice coefficient calculated on the remaining brains improved by 12 percent.

Kevin Raina

2020-12-04