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Radiology Radiology

Automatic segmentation, classification and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

In Medical physics ; h5-index 59.0

PURPOSE : Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using MRI is crucial for therapeutic decision-making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI.

METHODS : A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A 5-fold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel based classification of the tumor into enhanced, non-enhanced and cystic components was performed using fuzzy c-means clustering.

RESULTS : The results of the automatic tumor segmentation were: mean dice score=0.736±0.025, precision=0.918±0.014, and recall=0.635±0.039 for the validation data, and dice score =0.761±0.011, precision=0.794±0.028, and recall=0.742±0.012 for the test data. The accuracy of the voxel based classification of tumor components was 0.94, with precision=0.89, 0.97, 0.85 and recall= 1.00, 0.79, 0.94 for the non-enhanced, enhanced and cystic components, respectively.

CONCLUSION : This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Artzi Moran, Gershov Sapir, Ben-Sira Liat, Roth Jonathan, Kozyrev Danil, Shofty Ben, Gazit Tomer, Halag-Milo Tali, Constantini Shlomi, Ben Bashat Dafna

2020-Sep-23

Deep learning, Fuzzy c-means clustering, Optic pathway gliomas, Segmentation

General General

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning.

In Cancers

Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Mohammadi Elyas, Benfeitas Rui, Turkez Hasan, Boren Jan, Nielsen Jens, Uhlen Mathias, Mardinoglu Adil

2020-Sep-21

drug repositioning, genomic screens, machine learning, systems medicine, systems pharmacology

Radiology Radiology

Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis.

In Experimental and therapeutic medicine

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

Trivizakis Eleftherios, Tsiknakis Nikos, Vassalou Evangelia E, Papadakis Georgios Z, Spandidos Demetrios A, Sarigiannis Dimosthenis, Tsatsakis Aristidis, Papanikolaou Nikolaos, Karantanas Apostolos H, Marias Kostas

2020-Nov

COVID-19, artificial intelligence, deep learning analysis, multi-institutional data

General General

Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data

ArXiv Preprint

Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. The high-resolution longitudinal activity data collected by smartphone applications nowadays make it possible to conduct remote and convenient health assessment. However, out-of-lab tests often suffer from poor quality controls as well as irregularly collected observations, leading to noisy test results. To address these issues, we propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild. The proposed method first synchronizes discrete activity tests into multimodal features at unified time points. Next, it distills and enriches local and global representations from noisy data across modalities and temporal observations by two attention modules. With the proposed mechanisms, our model is capable of handling noisy observations and at the same time extracting refined temporal features for improved prediction performance. Quantitative and qualitative results on a large public dataset demonstrate the effectiveness of the proposed approach.

Weijian Li, Wei Zhu, E. Ray Dorsey, Jiebo Luo

2020-09-25

Surgery Surgery

Empowering Caseworkers to Better Serve the Most Vulnerable with a Cloud-Based Care Management Solution.

In Applied clinical informatics ; h5-index 22.0

BACKGROUND :  Care-management tools are typically utilized for chronic disease management. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals.

OBJECTIVES :  The objective of this case report is to describe how an integrated data hub and care-management solution streamlined care coordination of government services during a time of community-wide crisis.

METHODS :  This innovative application of care-management tools created a bridge between social and clinical determinants of health and used a three-step approach-access, collaboration, and innovation. The program Accessing Coordinated Care to Empower Self Sufficiency Sonoma was established to identify and match the most vulnerable residents with services to improve their well-being. Sonoma County created an Interdepartmental Multidisciplinary Team to deploy coordinated cross-departmental services (e.g., health and human services, housing services, probation) to support individuals experiencing housing insecurity. Implementation of a data integration hub (DIH) and care management and coordination system (CMCS) enabled integration of siloed data and services into a unified view of citizen status, identification of clinical and social determinants of health from structured and unstructured sources, and algorithms to match clients across systems.

RESULTS :  The integrated toolset helped 77 at-risk individuals in crisis through coordinated care plans and access to services in a time of need. Two case examples illustrate the specific care and services provided individuals with complex needs after the 2017 Sonoma County wildfires.

CONCLUSION :  Unique application of a care-management solution transformed health and health care for individuals fleeing from their homes and socially disadvantaged groups displaced by the Sonoma County wildfires. Future directions include expanding the DIH and CMCS to neighboring counties to coordinate care regionally. Such solutions might enable innovative care-management solutions across a variety of public, private, and nonprofit services.

Snowdon Jane L, Robinson Barbie, Staats Carolyn, Wolsey Kenneth, Sands-Lincoln Megan, Strasheim Thomas, Brotman David, Keating Katie, Schnitter Elizabeth, Jackson Gretchen, Kassler William

2020-Aug

General General

Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study.

In Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract

BACKGROUND AND AIMS : Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment.

METHODS : The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov . (NCT047126265).

RESULTS : In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions.

CONCLUSIONS : A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion.

TRIAL REGISTRATION : clinicaltrials.gov Identifier: NCT047126265.

Luo Yuchen, Zhang Yi, Liu Ming, Lai Yihong, Liu Panpan, Wang Zhen, Xing Tongyin, Huang Ying, Li Yue, Li Aiming, Wang Yadong, Luo Xiaobei, Liu Side, Han Zelong

2020-Sep-23

Artificial intelligence, Colonoscopy, Computer-aided diagnose