In Journal of biomedical optics

SIGNIFICANCE : Automatic and accurate classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images is essential for assisting ophthalmologist in the diagnosis and grading of macular diseases. Therefore, more effective OCT volume classification for automatic recognition of macular diseases is needed.

AIM : For OCT volumes in which only OCT volume-level labels are known, OCT volume classifiers based on its global feature and deep learning are designed, validated, and compared with other methods.

APPROACH : We present a general framework to classify OCT volume for automatic recognizing macular diseases. The architecture of the framework consists of three modules: B-scan feature extractor, two-dimensional (2-D) feature map generation, and volume-level classifier. Our architecture could address OCT volume classification using two 2-D image machine learning classification algorithms. Specifically, a convolutional neural network (CNN) model is trained and used as a B-scan feature extractor to construct a 2-D feature map of an OCT volume and volume-level classifiers such as support vector machine and CNN with/without attention mechanism for 2-D feature maps are described.

RESULTS : Our proposed methods are validated on the publicly available Duke dataset, which consists of 269 intermediate age-related macular degeneration (AMD) volumes and 115 normal volumes. Fivefold cross-validation was done, and average accuracy, sensitivity, and specificity of 98.17%, 99.26%, and 95.65%, respectively, are achieved. The experiments show that our methods outperform the state-of-the-art methods. Our methods are also validated on our private clinical OCT volume dataset, consisting of 448 AMD volumes and 462 diabetic macular edema volumes.

CONCLUSIONS : We present a general framework of OCT volume classification based on its 2-D feature map and CNN with attention mechanism and describe its implementation schemes. Our proposed methods could classify OCT volumes automatically and effectively with high accuracy, and they are a potential practical tool for screening of ophthalmic diseases from OCT volume.

Sun Yankui, Zhang Haoran, Yao Xianlin

2020-Sep

attention mechanism, convolutional neural network, image classification, optical coherence tomography, transfer learning

In Proteomics

Wen Bo, Zeng Wenfeng, Liao Yuxing, Shi Zhiao, Savage Sara R, Jiang Wen, Zhang Bing

2020-Sep-16

bioinformatics, deep learning, proteomics

In European journal of clinical investigation

BACKGROUND : Today, numerous countries are fighting to protect themselves against the Covid-19 crisis, while the policymakers are confounded and empty-handed in dealing with this chaotic circumstance. The infection and its impacts have made it difficult to make optimal and suitable decisions. New information technologies play significant roles in such critical situations to address and relieve stress during the coronavirus crisis. This article endeavors to recognize the challenges policymakers have typically experienced during pandemic diseases, including Covid-19, and, accordingly, new information technology capabilities to encounter with them.

MATERIAL AND METHODS : The current study utilizes the synthesis of findings of experts' opinions within the systematic review process as the research method to recognize the best available evidence drawn from text and opinion to offer practical guidance for policymakers.

RESULTS : The results illustrate that the challenges fall into two categories including; encountering the disease and reducing the results of the disease. Furthermore, Internet of Things, cloud computing, machine learning, and social networking play the most significant roles to address these challenges.

Lagzian Mohammad, Dadkhah Mehdi, Mehraeen AmirReza

2020-Sep-16

Covid-19, Crisis management policies, Informational Technology (IT) capabilities, Pandemic management

In Rheumatology and therapy

INTRODUCTION : The performance of seven cardiovascular (CV) risk algorithms is evaluated in a multicentric cohort of ankylosing spondylitis (AS) patients. Performance and calibration of traditional CV predictors have been compared with the novel paradigm of machine learning (ML).

METHODS : A retrospective analysis of prospectively collected data from an AS cohort has been performed. The primary outcome was the first CV event. The discriminatory ability of the algorithms was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), which is like the concordance-statistic (c-statistic). Three ML techniques were considered to calculate the CV risk: support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN).

RESULTS : Of 133 AS patients enrolled, 18 had a CV event. c-statistic scores of 0.71, 0.61, 0.66, 0.68, 0.66, 0.72, and 0.67 were found, respectively, for SCORE, CUORE, FRS, QRISK2, QRISK3, RRS, and ASSIGN. AUC values for the ML algorithms were: 0.70 for SVM, 0.73 for RF, and 0.64 for KNN. Feature analysis showed that C-reactive protein (CRP) has the highest importance, while SBP and hypertension treatment have lower importance.

CONCLUSIONS : All of the evaluated CV risk algorithms exhibit a poor discriminative ability, except for RRS and SCORE, which showed a fair performance. For the first time, we demonstrated that AS patients do not show the traditional ones used by CV scores and that the most important variable is CRP. The present study contributes to a deeper understanding of CV risk in AS, allowing the development of innovative CV risk patient-specific models.

Navarini Luca, Caso Francesco, Costa Luisa, Currado Damiano, Stola Liliana, Perrotta Fabio, Delfino Lorenzo, Sperti Michela, Deriu Marco A, Ruscitti Piero, Pavlych Viktoriya, Corrado Addolorata, Di Benedetto Giacomo, Tasso Marco, Ciccozzi Massimo, Laudisio Alice, Lunardi Claudio, Cantatore Francesco Paolo, Lubrano Ennio, Giacomelli Roberto, Scarpa Raffaele, Afeltra Antonella

2020-Sep-16

Ankylosing spondylitis, C-reactive protein, Cardiovascular risk, Machine learning

In Abdominal radiology (New York)

PURPOSE : To determine equivalency of multi-slice 3D CTTA and single slice 2D CTTA of pancreas adenocarcinoma.

METHODS : This retrospective study was research ethics board approved. Untreated pancreas adenocarcinomas were segmented on CT in 128 consecutive patients. Tumor segmentation was compared using two techniques: 3D segmentation by contouring all visible tumor in a 3D volume, and 2D segmentation using only a single axial image. First-order CTTA features including mean, minimum, maximum Hounsfield units (HU), standard deviation, skewness, kurtosis, entropy, and second-order gray-level co-occurrence matrix (GLCM) features homogeneity, contrast, correlation, entropy and dissimilarity were extracted. Median values were compared using the Mann-Whitney U test with Holm-Bonferroni correction. Kendall's Rank Correlation Tau assessed for correlation, and agreement was calculated using intraclass correlation coefficients (ICC) using a two-way model with single rating and absolute agreement. Statistical significance defined as P < 0.05.

RESULTS : The median values of CTTA features differed significantly between 3 and 2D segmentations for all of the evaluated features except for mean attenuation, standard deviation and skewness (P = 0.2979 each). 3D and 2D segmentations had moderate correlation for mean attenuation (R = 0.69, P < 0.01), while all other features demonstrated poor to fair correlation. Agreement between 3 and 2D segmentations was good for mean attenuation (ICC: 0.87, P < 0.01), moderate for minimum (ICC: 0.65, P < 0.01) and standard deviation (ICC: 0.56, P < 0.01), and poor for all other features.

CONCLUSION : While pancreas adenocarcinoma CTTA features obtained using 3D and 2D segmentation have multiple associations with clinically relevant outcomes, these segmentation techniques are likely not interchangeable other than for mean HU.

Kulkarni Ameya, Carrion-Martinez Ivan, Dhindsa Kiret, Alaref Amer A, Rozenberg Radu, van der Pol Christian B

2020-Sep-16

Carcinoma, Methods, Multidetector computed tomography, Pancreatic ductal, Software

In Abdominal radiology (New York)

PURPOSE : To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance.

METHODS : In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists' segmentations were compared against radiologists' segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland-Altman analysis.

RESULTS : Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [- 2.74 cc (min - 92.96 cc, max 87.47 cc) versus - 23.57 cc (min - 77.32, max 30.19)].

CONCLUSION : Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.

Suman Garima, Panda Ananya, Korfiatis Panagiotis, Edwards Marie E, Garg Sushil, Blezek Daniel J, Chari Suresh T, Goenka Ajit H

2020-Sep-16

Artificial intelligence, COVID-19, Data curation, Deep learning