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Radiology Radiology

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier.

In Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.

Kaka Hussam, Zhang Euan, Khan Nazir

2020-Sep-18

artificial, deep, intelligence, learning, neuroradiology

General General

From ionic surfactants to Nafion through convolutional neural networks.

In The journal of physical chemistry. B

We have applied recent machine learning advances, deep convolutional neural network, to three-dimensional (voxels) soft matter data, generated by Molecular Dynamics computer simulation. We have focused on the structural and phase properties of a coarse-grained model of hydrated ionic surfactants. We have trained a classifier able to automatically detect the water quantity absorbed in the system, therefore associating to each hydration level the corresponding most representative nano-structure. Based on the notion of transfer learning, we have next applied the same network to the related polymeric ionomer Nafion, and have extracted a measure of the similarity of these configurations with those above. We demonstrate that on this basis it is possible to express the static structure factor of the polymer at fixed hydration level as a superposition of those of the surfactants at multiple water contents. We suggest that such a procedure can provide a useful, agnostic, data-driven, precise description of the multi-scale structure of disordered materials, without resorting to any a-priori model picture.

Dumortier Loic, Mossa Stefano

2020-Sep-18

General General

Technology outlook for real-time quality attribute and process parameter monitoring in biopharmaceutical development-A review.

In Biotechnology and bioengineering

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.

Wasalathanthri Dhanuka P, Rehmann Matthew S, Song Yuanli, Gu Yan, Mi Luo, Shao Chun, Chemmalil Letha, Lee Jongchan, Ghose Sanchayita, Borys Michael C, Ding Julia, Li Zheng Jian

2020-Oct

process analytical technology, quality by design, real‐time monitoring

Radiology Radiology

Artificial intelligence in cardiac radiology.

In La Radiologia medica

Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.

van Assen Marly, Muscogiuri Giuseppe, Caruso Damiano, Lee Scott J, Laghi Andrea, De Cecco Carlo N

2020-Sep-18

Artificial intelligence, Cardiac imaging, Computed tomography, Magnetic resonance imaging

Radiology Radiology

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks.

In European radiology ; h5-index 62.0

OBJECTIVES : Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging.

METHODS : Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits.

RESULTS : The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99.

CONCLUSIONS : Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine.

KEY POINTS : • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

Koitka Sven, Kroll Lennard, Malamutmann Eugen, Oezcelik Arzu, Nensa Felix

2020-Sep-18

Abdomen, Body composition, Computer-assisted image analysis, Deep learning

Radiology Radiology

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma.

In Journal of personalized medicine

Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM.

Le Nguyen Quoc Khanh, Do Duyen Thi, Chiu Fang-Ying, Yapp Edward Kien Yee, Yeh Hui-Yuan, Chen Cheng-Yu

2020-Sep-15

F-score feature selection, IDH1 wildtype, O6-methylguanine-DNA methyltransferase, XGBoost, concomitant adjuvant temozolomide, glioblastoma, machine learning, molecular subtype, noninvasive imaging biomarker, radiogenomics