7901
|
Li HH, Bao ZX, Liu XB, Zhu SH. [Advances in the research of application of artificial intelligence in burn field]. Zhonghua Shao Shang Za Zhi 2018; 34:246-248. [PMID: 29690744 DOI: 10.3760/cma.j.issn.1009-2587.2018.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Artificial intelligence has been able to automatically learn and judge large-scale data to some extent. Based on database of a large amount of burn data and in-depth learning, artificial intelligence can assist burn surgeons to evaluate burn surface, diagnose burn depth, guide fluid supply during shock stage, and predict prognosis, with high accuracy. With the development of technology, artificial intelligence can provide more accurate information for burn surgeons to make clinical diagnosis and treatment strategies.
Collapse
Affiliation(s)
- H H Li
- Department of Burn Surgery, Institute of Burns, the first affiliated Hospital of Naval Medical University, Shanghai 200433, China
| | | | | | | |
Collapse
|
7902
|
Chaari M, Fekih A, Seibi AC, Hmida JB. A frequency-domain approach to improve ANNs generalization quality via proper initialization. Neural Netw 2018; 104:26-39. [PMID: 29705668 DOI: 10.1016/j.neunet.2018.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 03/12/2018] [Accepted: 04/08/2018] [Indexed: 11/19/2022]
Abstract
The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach.
Collapse
Affiliation(s)
- Majdi Chaari
- Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, P.O. Box 43890, Lafayette, LA 70504, USA.
| | - Afef Fekih
- Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, P.O. Box 43890, Lafayette, LA 70504, USA.
| | - Abdennour C Seibi
- Department of Petroleum Engineering, University of Louisiana at Lafayette, P.O. Box 44690, Lafayette, LA 70504, USA.
| | - Jalel Ben Hmida
- Department of Mechanical Engineering, University of Louisiana at Lafayette, P.O. Box 43678, Lafayette, LA 70504, USA.
| |
Collapse
|
7903
|
Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res 2018; 228:179-187. [PMID: 29907209 DOI: 10.1016/j.jss.2018.03.028] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/07/2018] [Accepted: 03/14/2018] [Indexed: 12/23/2022]
Abstract
BACKGROUND Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement. MATERIALS AND METHODS The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all intensive care unit (ICU) stays with mechanical ventilation. PMV was defined as ventilation >7 d. Classifiers with a gradient-boosted decision trees algorithm were created for the outcomes of PMV and tracheostomy placement. The variables used were six different severity-of-illness scores calculated on the first day of ICU admission including their components and 30 comorbidities. Mean receiver operating characteristic curves were calculated for the outcomes, and variable importance was quantified. RESULTS There were 20,262 ICU stays identified. PMV was required in 13.6%, and tracheostomy was performed in 6.6% of patients. The classifier for predicting PMV was able to achieve a mean area under the curve (AUC) of 0.820 ± 0.016, and tracheostomy was predicted with an AUC of 0.830 ± 0.011. There were 60.7% patients admitted to a surgical ICU, and the classifiers for these patients predicted PMV with an AUC of 0.852 ± 0.017 and tracheostomy with an AUC of 0.869 ± 0.015. The variable with the highest importance for predicting PMV was the logistic organ dysfunction score pulmonary component (13%), and the most important comorbidity in predicting tracheostomy was cardiac arrhythmia (12%). CONCLUSIONS This study demonstrates the use of artificial intelligence through machine-learning classifiers for the early identification of patients at risk for PMV and tracheostomy. Application of these identification techniques could lead to improved outcomes by allowing for early intervention.
Collapse
Affiliation(s)
- Joshua Parreco
- DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida
| | - Antonio Hidalgo
- DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida
| | - Jonathan J Parks
- DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida
| | - Robert Kozol
- DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida
| | - Rishi Rattan
- Division of Trauma Surgery and Surgical Critical Care, DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
| |
Collapse
|
7904
|
Desai GS. Artificial Intelligence: The Future of Obstetrics and Gynecology. J Obstet Gynaecol India 2018; 68:326-7. [PMID: 30065551 DOI: 10.1007/s13224-018-1118-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/27/2018] [Indexed: 12/15/2022] Open
Abstract
Background Artificial intelligence or 'big data' comprises of algorithms which aid in decision making. It has made an impact on a number of professions including obstetrics and gynecology. Objective To make readers aware of where artificial intelligence has a role in obstetrics and gynecology. Material and methods A comprehensive review of the literature was undertaken to compile a list of instances where artificial intelligence was applied to obstetrics and gynecology. Conclusion Artificial intelligence should be utilized to benefit patient care and assist the physician in providing data for decision making.
Collapse
|
7905
|
Abstract
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.
Collapse
Affiliation(s)
| | | | | | - Timothy Kline
- Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | | |
Collapse
|
7906
|
Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, Do S. Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis. J Digit Imaging 2018; 30:487-498. [PMID: 28653123 PMCID: PMC5537099 DOI: 10.1007/s10278-017-9988-z] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an “eyeball test” to assess whether patients will tolerate major surgery or chemotherapy, “eyeballing” is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts. In this study, we propose a fully automated deep learning system for the segmentation of skeletal muscle cross-sectional area (CSA) on an axial computed tomography image taken at the third lumbar vertebra. We utilized a fully automated deep segmentation model derived from an extended implementation of a fully convolutional network with weight initialization of an ImageNet pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis. This experiment was conducted by varying window level (WL), window width (WW), and bit resolutions in order to better understand the effects of the parameters on the model performance. Our best model, fine-tuned on 250 training images and ground truth labels, achieves 0.93 ± 0.02 Dice similarity coefficient (DSC) and 3.68 ± 2.29% difference between predicted and ground truth muscle CSA on 150 held-out test cases. Ultimately, the fully automated segmentation system can be embedded into the clinical environment to accelerate the quantification of muscle and expanded to volume analysis of 3D datasets.
Collapse
Affiliation(s)
- Hyunkwang Lee
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114 USA
| | - Fabian M. Troschel
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114 USA
| | - Shahein Tajmir
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114 USA
| | - Georg Fuchs
- Department of Radiology, Charite - Universitaetsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Julia Mario
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114 USA
| | - Florian J. Fintelmann
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114 USA
| | - Synho Do
- Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Suite 400B, Boston, MA 02114 USA
| |
Collapse
|
7907
|
Abstract
The goal of this study is to evaluate the efficacy of deep convolutional neural networks (DCNNs) in differentiating subtle, intermediate, and more obvious image differences in radiography. Three different datasets were created, which included presence/absence of the endotracheal (ET) tube (n = 300), low/normal position of the ET tube (n = 300), and chest/abdominal radiographs (n = 120). The datasets were split into training, validation, and test. Both untrained and pre-trained deep neural networks were employed, including AlexNet and GoogLeNet classifiers, using the Caffe framework. Data augmentation was performed for the presence/absence and low/normal ET tube datasets. Receiver operating characteristic (ROC), area under the curves (AUC), and 95% confidence intervals were calculated. Statistical differences of the AUCs were determined using a non-parametric approach. The pre-trained AlexNet and GoogLeNet classifiers had perfect accuracy (AUC 1.00) in differentiating chest vs. abdominal radiographs, using only 45 training cases. For more difficult datasets, including the presence/absence and low/normal position endotracheal tubes, more training cases, pre-trained networks, and data-augmentation approaches were helpful to increase accuracy. The best-performing network for classifying presence vs. absence of an ET tube was still very accurate with an AUC of 0.99. However, for the most difficult dataset, such as low vs. normal position of the endotracheal tube, DCNNs did not perform as well, but achieved a reasonable AUC of 0.81.
Collapse
Affiliation(s)
- Paras Lakhani
- Thomas Jefferson University Hospital, Sidney Kimmel Jefferson Medical College, Philadelphia, PA, 19107, USA.
| |
Collapse
|
7908
|
Cath C, Wachter S, Mittelstadt B, Taddeo M, Floridi L. Artificial Intelligence and the 'Good Society': the US, EU, and UK approach. Sci Eng Ethics 2018; 24:505-528. [PMID: 28353045 DOI: 10.1007/s11948-017-9901-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Accepted: 03/19/2017] [Indexed: 06/06/2023]
Abstract
In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.
Collapse
Affiliation(s)
- Corinne Cath
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK.
- The Alan Turing Institute, Headquartered at the British Library, 96 Euston Road, London, NW1 2DB, UK.
| | - Sandra Wachter
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
- The Alan Turing Institute, Headquartered at the British Library, 96 Euston Road, London, NW1 2DB, UK
| | - Brent Mittelstadt
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
- The Alan Turing Institute, Headquartered at the British Library, 96 Euston Road, London, NW1 2DB, UK
| | - Mariarosaria Taddeo
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
- The Alan Turing Institute, Headquartered at the British Library, 96 Euston Road, London, NW1 2DB, UK
| | - Luciano Floridi
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
- The Alan Turing Institute, Headquartered at the British Library, 96 Euston Road, London, NW1 2DB, UK
| |
Collapse
|
7909
|
Abstract
Experts in medical informatics have argued for the incorporation of ever more machine-learning algorithms into medical care. As artificial intelligence (AI) research advances, such technologies raise the possibility of an "iDoctor," a machine theoretically capable of replacing the judgment of primary care physicians. In this article, I draw on Martin Heidegger's critique of technology to show how an algorithmic approach to medicine distorts the physician-patient relationship. Among other problems, AI cannot adapt guidelines according to the individual patient's needs. In response to the objection that AI could develop this capacity, I use Hubert Dreyfus's analysis of AI to argue that attention to the needs of each patient requires the physician to attune his or her perception to the patient's history and physical exam, an ability that seems uniquely human. Human physician judgment will remain better suited to the practice of primary care despite anticipated advances in AI technology.
Collapse
Affiliation(s)
- Kyle E Karches
- Department of Internal Medicine, Saint Louis University Hospital, 3635 Vista Ave, Saint Louis, MO, 63110, USA.
| |
Collapse
|
7910
|
Nedungadi P, Iyer A, Gutjahr G, Bhaskar J, Pillai AB. Data-Driven Methods for Advancing Precision Oncology. Curr Pharmacol Rep 2018; 4:145-156. [PMID: 33520605 PMCID: PMC7845924 DOI: 10.1007/s40495-018-0127-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
PURPOSE OF REVIEW This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice. RECENT FINDINGS Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care. SUMMARY Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.
Collapse
Affiliation(s)
- Prema Nedungadi
- Center for Research in Analytics & Technology in Education, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India
- Department of Computer Science, School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India
| | - Akshay Iyer
- Center for Research in Analytics & Technology in Education, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India
| | - Georg Gutjahr
- Center for Research in Analytics & Technology in Education, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India
| | - Jasmine Bhaskar
- Center for Research in Analytics & Technology in Education, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India
- Department of Computer Science, School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, India
| | - Asha B. Pillai
- Division of Pediatric Hematology/Oncology, Departments of Pediatrics and Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, USA
| |
Collapse
|
7911
|
Jia W, Li Y, Qu R, Baranowski T, Burke LE, Zhang H, Bai Y, Mancino JM, Xu G, Mao ZH, Sun M. Automatic food detection in egocentric images using artificial intelligence technology. Public Health Nutr 2019; 22:1168-79. [PMID: 29576027 DOI: 10.1017/S1368980018000538] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. DESIGN To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. RESULTS A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. CONCLUSIONS The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.
Collapse
|
7912
|
Zhang X. [New Concept of the Development of Modern Medicine: Make Full Use of the Internet, Large Data, and Artificial Intelligence]. Zhongguo Fei Ai Za Zhi 2018; 21:141-2. [PMID: 29587927 DOI: 10.3779/j.issn.1009-3419.2018.03.02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
7913
|
Nakajima K, Okuda K, Watanabe S, Matsuo S, Kinuya S, Toth K, Edenbrandt L. Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database. Ann Nucl Med 2018; 32:303-10. [PMID: 29516390 DOI: 10.1007/s12149-018-1247-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Purpose An artificial neural network (ANN) has been applied to detect myocardial perfusion defects and ischemia. The present study compares the diagnostic accuracy of a more recent ANN version (1.1) with the initial version 1.0. Methods We examined 106 patients (age, 77 ± 10 years) with coronary angiographic findings, comprising multi-vessel disease (≥ 50% stenosis) (52%) or old myocardial infarction (27%), or who had undergone coronary revascularization (30%). The ANN versions 1.0 and 1.1 were trained in Sweden (n = 1051) and Japan (n = 1001), respectively, using 99mTc-methoxyisobutylisonitrile myocardial perfusion images. The ANN probabilities (from 0.0 to 1.0) of stress defects and ischemia were calculated in candidate regions of abnormalities. The diagnostic accuracy was compared using receiver-operating characteristics (ROC) analysis and the calculated area under the ROC curve (AUC) using expert interpretation as the gold standard. Results Although the AUC for stress defects was 0.95 and 0.93 (p = 0.27) for versions 1.1 and 1.0, respectively, that for detecting ischemia was significantly improved in version 1.1 (p = 0.0055): AUC 0.96 for version 1.1 (sensitivity 87%, specificity 96%) vs. 0.89 for version 1.0 (sensitivity 78%, specificity 97%). The improvement in the AUC shown by version 1.1 was also significant for patients with neither coronary revascularization nor old myocardial infarction (p = 0.0093): AUC = 0.98 for version 1.1 (sensitivity 88%, specificity 100%) and 0.88 for version 1.0 (sensitivity 76%, specificity 100%). Intermediate ANN probability between 0.1 and 0.7 was more often calculated by version 1.1 compared with version 1.0, which contributed to the improved diagnostic accuracy. The diagnostic accuracy of the new version was also improved in patients with either single-vessel disease or no stenosis (n = 47; AUC, 0.81 vs. 0.66 vs. p = 0.0060) when coronary stenosis was used as a gold standard. Conclusion The diagnostic ability of the ANN version 1.1 was improved by retraining using the Japanese database, particularly for identifying ischemia.
Collapse
|
7914
|
Abstract
The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds.
Collapse
|
7915
|
Yoshida H, Shimazu T, Kiyuna T, Marugame A, Yamashita Y, Cosatto E, Taniguchi H, Sekine S, Ochiai A. Automated histological classification of whole-slide images of gastric biopsy specimens. Gastric Cancer 2018; 21:249-257. [PMID: 28577229 DOI: 10.1007/s10120-017-0731-8] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 05/25/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND Automated image analysis has been developed currently in the field of surgical pathology. The aim of the present study was to evaluate the classification accuracy of the e-Pathologist image analysis software. METHODS A total of 3062 gastric biopsy specimens were consecutively obtained and stained. The specimen slides were anonymized and digitized. At least two experienced gastrointestinal pathologists evaluated each slide for pathological diagnosis. We compared the three-tier (positive for carcinoma or suspicion of carcinoma; caution for adenoma or suspicion of a neoplastic lesion; or negative for a neoplastic lesion) or two-tier (negative or non-negative) classification results of human pathologists and of the e-Pathologist. RESULTS Of 3062 cases, 33.4% showed an abnormal finding. For the three-tier classification, the overall concordance rate was 55.6% (1702/3062). The kappa coefficient was 0.28 (95% CI, 0.26-0.30; fair agreement). For the negative biopsy specimens, the concordance rate was 90.6% (1033/1140), but for the positive biopsy specimens, the concordance rate was less than 50%. For the two-tier classification, the sensitivity, specificity, positive predictive value, and negative predictive value were 89.5% (95% CI, 87.5-91.4%), 50.7% (95% CI, 48.5-52.9%), 47.7% (95% CI, 45.4-49.9%), and 90.6% (95% CI, 88.8-92.2%), respectively. CONCLUSIONS Although there are limitations and requirements for applying automated histopathological classification of gastric biopsy specimens in the clinical setting, the results of the present study are promising.
Collapse
Affiliation(s)
- Hiroshi Yoshida
- Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Taichi Shimazu
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Tomoharu Kiyuna
- Medical Solutions Division, NEC Corporation, 5-7-1 Shiba, Minato-ku, Tokyo, 108-8001, Japan
| | - Atsushi Marugame
- Space System Division, NEC Corporation, 10, Nisshin-cho 1-Chome, Fuchu, Tokyo, 183-8501, Japan
| | - Yoshiko Yamashita
- Medical Solutions Division, NEC Corporation, 5-7-1 Shiba, Minato-ku, Tokyo, 108-8001, Japan
| | - Eric Cosatto
- Department of Machine Learning, NEC Laboratories America, 4 Independence Way, Suite 200, Princeton, NJ, 08540, USA
| | - Hirokazu Taniguchi
- Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shigeki Sekine
- Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Division of Molecular Pathology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Atsushi Ochiai
- Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Division of Pathology, Research Center for Innovative Oncology, National Cancer Center, 6-5-1, Kashiwa, Chiba, 277-8577, Japan
| |
Collapse
|
7916
|
Gomes CP, Salgado-Somoza A, Creemers EE, Dieterich C, Lustrek M, Devaux Y. Circular RNAs in the cardiovascular system. Noncoding RNA Res 2018; 3:1-11. [PMID: 30159434 PMCID: PMC6084836 DOI: 10.1016/j.ncrna.2018.02.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/16/2018] [Accepted: 02/22/2018] [Indexed: 02/06/2023] Open
Abstract
Until recently considered as rare, circular RNAs (circRNAs) are emerging as important regulators of gene expression. They are ubiquitously expressed and represent a novel branch of the family of non-coding RNAs. Recent investigations showed that circRNAs are regulated in the cardiovascular system and participate in its physiological and pathological development. In this review article, we will provide an overview of the role of circRNAs in cardiovascular health and disease. After a description of the biogenesis of circRNAs, we will summarize what is known of the expression, regulation and function of circRNAs in the cardiovascular system. We will then address some technical aspects of circRNAs research, discussing how artificial intelligence may aid in circRNAs research. Finally, the potential of circRNAs as biomarkers of cardiovascular disease will be addressed and directions for future research will be proposed.
Collapse
Key Words
- Artificial intelligence
- Biomarker
- CRISPR, clustered regularly interspaced short palindromic repeats
- CV, cardiovascular
- Cardiovascular disease
- Cardiovascular system
- Circular RNAs
- DCM, dilated cardiomyopathy
- EMT, epithelial-mesenchymal transition
- Non-coding RNAs
- RNA-seq, RNA sequencing
- RPAD, RNase R treatment followed by polyadenylation and poly(A)+ RNA depletion
- RT-qPCR, reverse transcription quantitative polymerase chain reaction
- circRNAs, circular RNAs
- lncRNAs, long non-coding RNAs
- miRNAs, microRNAs
- ncRNAs, non-coding RNAs
Collapse
Affiliation(s)
- Clarissa P.C. Gomes
- Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | | | - Esther E. Creemers
- Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands
| | - Christoph Dieterich
- German Center for Cardiovascular Research, University Hospital Heidelberg, Heidelberg, Germany
| | - Mitja Lustrek
- Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Yvan Devaux
- Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | | |
Collapse
|
7917
|
Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M, Sheppard DW. Big Data in Drug Discovery. Prog Med Chem 2018; 57:277-356. [PMID: 29680150 DOI: 10.1016/bs.pmch.2017.12.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Aaron Sim
- BenevolentAI, London, United Kingdom
| | | | - Magdalena Zwierzyna
- BenevolentAI, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
| | | |
Collapse
|
7918
|
Parreco JP, Hidalgo AE, Badilla AD, Ilyas O, Rattan R. Predicting central line-associated bloodstream infections and mortality using supervised machine learning. J Crit Care 2018; 45:156-62. [PMID: 29486341 DOI: 10.1016/j.jcrc.2018.02.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 02/16/2018] [Accepted: 02/17/2018] [Indexed: 11/22/2022]
Abstract
PURPOSE The purpose of this study was to compare machine learning techniques for predicting central line-associated bloodstream infection (CLABSI). MATERIALS AND METHODS The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all ICU admissions. The variables included six different severities of illness scores calculated on the first day of ICU admission with their components and comorbidities. The outcomes of interest were in-hospital mortality, central line placement, and CLABSI. Predictive models were created for these outcomes using classifiers with different algorithms: logistic regression, gradient boosted trees, and deep learning. RESULTS There were 57,786 total hospital admissions and the mortality rate was 10.1%. There were 38.4% patients with a central line and the rate of CLABSI was 1.5%. The classifiers using deep learning performed with the highest AUC for mortality, 0.885±0.010 (p<0.01) and central line placement, 0.816±0.006 (p<0.01). The classifier using logistic regression for predicting CLABSI performed with an AUC of 0.722±0.048 (p<0.01). CONCLUSIONS This study demonstrates models for identifying patients who will develop CLABSI. Early identification of these patients has implications for quality, cost, and outcome improvements.
Collapse
|
7919
|
Collado-Mesa F, Alvarez E, Arheart K. The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program. J Am Coll Radiol 2018; 15:1753-1757. [PMID: 29477289 DOI: 10.1016/j.jacr.2017.12.021] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/20/2022]
Abstract
PURPOSE Advances in artificial intelligence applied to diagnostic radiology are predicted to have a major impact on this medical specialty. With the goal of establishing a baseline upon which to build educational activities on this topic, a survey was conducted among trainees and attending radiologists at a single residency program. METHODS An anonymous questionnaire was distributed. Comparisons of categorical data between groups (trainees and attending radiologists) were made using Pearson χ2 analysis or an exact analysis when required. Comparisons were made using the Wilcoxon rank sum test when the data were not normally distributed. An α level of 0.05 was used. RESULTS The overall response rate was 66% (69 of 104). Thirty-six percent of participants (n = 25) reported not having read a scientific medical article on the topic of artificial intelligence during the past 12 months. Twenty-nine percent of respondents (n = 12) reported using artificial intelligence tools during their daily work. Trainees were more likely to express doubts on whether they would have pursued diagnostic radiology as a career had they known of the potential impact artificial intelligence is predicted to have on the specialty (P = .0254) and were also more likely to plan to learn about the topic (P = .0401). CONCLUSIONS Radiologists lack exposure to current scientific medical articles on artificial intelligence. Trainees are concerned by the implications artificial intelligence may have on their jobs and desire to learn about the topic. There is a need to develop educational resources to help radiologists assume an active role in guiding and facilitating the development and implementation of artificial intelligence tools in diagnostic radiology.
Collapse
Affiliation(s)
- Fernando Collado-Mesa
- Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida.
| | - Edilberto Alvarez
- Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida
| | - Kris Arheart
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida
| |
Collapse
|
7920
|
Abstract
Until now, most major medical advancements have been achieved through hypothesis-driven research within the scope of clinical trials. However, due to a multitude of variables, only a certain number of research questions could be addressed during a single study, thus rendering these studies expensive and time consuming. Big data acquisition enables a new data-based approach in which large volumes of data can be used to investigate all variables, thus opening new horizons. Due to universal digitalization of the data as well as ever-improving hard- and software solutions, imaging would appear to be predestined for such analyses. Several small studies have already demonstrated that automated analysis algorithms and artificial intelligence can identify pathologies with high precision. Such automated systems would also seem well suited for rheumatology imaging, since a method for individualized risk stratification has long been sought for these patients. However, despite all the promising options, the heterogeneity of the data and highly complex regulations covering data protection in Germany would still render a big data solution for imaging difficult today. Overcoming these boundaries is challenging, but the enormous potential advances in clinical management and science render pursuit of this goal worthwhile.
Collapse
Affiliation(s)
- Philipp Sewerin
- Heinrich-Heine-Universität Düsseldorf (HHU), Poliklinik, Funktionsbereich und Hiller-Forschungszentrum für Rheumatologie, Universitätskliniken Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Deutschland.
| | - Benedikt Ostendorf
- Heinrich-Heine-Universität Düsseldorf (HHU), Poliklinik, Funktionsbereich und Hiller-Forschungszentrum für Rheumatologie, Universitätskliniken Düsseldorf, Moorenstraße 5, 40225, Düsseldorf, Deutschland
| | - Axel J Hueber
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Medizinische Klinik 3 - Rheumatologie und Immunologie, Universitätsklinikum Erlangen, Erlangen, Deutschland
| | - Arnd Kleyer
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Medizinische Klinik 3 - Rheumatologie und Immunologie, Universitätsklinikum Erlangen, Erlangen, Deutschland
| |
Collapse
|
7921
|
Balthazar P, Harri P, Prater A, Safdar NM. Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics. J Am Coll Radiol 2018; 15:580-586. [PMID: 29402532 DOI: 10.1016/j.jacr.2017.11.035] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 11/27/2017] [Indexed: 12/12/2022]
Abstract
The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these principles in light of the potential issues surrounding privacy, confidentiality, data ownership, informed consent, epistemology, and inequities. Patients have strong opinions about these issues. Radiologists have a fiduciary responsibility to protect the interest of their patients. As such, the community of radiology leaders, ethicists, and informaticists must have a conversation about the appropriate way to deal with these issues and help lead the way in developing capabilities in the most just, ethical manner possible.
Collapse
Affiliation(s)
- Patricia Balthazar
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Peter Harri
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Adam Prater
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Nabile M Safdar
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.
| |
Collapse
|
7922
|
Vashistha R, Chhabra D, Shukla P. Integrated Artificial Intelligence Approaches for Disease Diagnostics. Indian J Microbiol 2018; 58:252-5. [PMID: 29651188 DOI: 10.1007/s12088-018-0708-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 01/06/2018] [Indexed: 12/13/2022] Open
Abstract
Mechanocomputational techniques in conjunction with artificial intelligence (AI) are revolutionizing the interpretations of the crucial information from the medical data and converting it into optimized and organized information for diagnostics. It is possible due to valuable perfection in artificial intelligence, computer aided diagnostics, virtual assistant, robotic surgery, augmented reality and genome editing (based on AI) technologies. Such techniques are serving as the products for diagnosing emerging microbial or non microbial diseases. This article represents a combinatory approach of using such approaches and providing therapeutic solutions towards utilizing these techniques in disease diagnostics.
Collapse
|
7923
|
Chari PS, Prasad S. Pilot Study on the Performance of a New System for Image Based Analysis of Peripheral Blood Smears on Normal Samples. Indian J Hematol Blood Transfus 2018; 34:125-131. [PMID: 29398811 DOI: 10.1007/s12288-017-0835-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 05/22/2017] [Indexed: 10/19/2022] Open
Abstract
Image analysis based automated systems aiming to automate the manual microscopic review of peripheral blood smears have gained popularity in recent times. In this paper, we evaluate a new blood smear analysis system based on artificial intelligence, Shonit™ by SigTuple Technologies Private Limited. One hundred normal samples with no flags from an automated haematology analyser were taken. Peripheral blood smear slides were prepared using the autostainer integrated with an automated haematology analyser and stained using May-Grunwald-Giemsa stain. These slides were analysed with Shonit™. The metrics for evaluation included (1) accuracy of white blood cell classification for the five normal white blood cell types, and (2) comparison of white blood cell differential count with the automated haematology analyser. In addition, we also explored the possibility of estimating the value of red blood cell and platelet indices via image analysis. Overall white blood cell classification specificity was greater than 97.90% and the precision was greater than 93.90% for all the five white blood cell classes. The correlation of the white blood cell differential count between the automated haematology analyser and Shonit™ was found to be within the known inter cell-counter variability. Shonit™ was found to show promise in terms of its ability to analyse peripheral blood smear images to derive quantifiable metrics useful for clinicians. Future enhancement should include the ability to analyse abnormal blood samples.
Collapse
Affiliation(s)
- Preethi S Chari
- Anand Diagnostic Laboratory, 54, Bowring Tower, Bowring Hospital Road, Shivajinagar, Bengaluru, Karnataka 560001 India
| | - Sujay Prasad
- Anand Diagnostic Laboratory, 54, Bowring Tower, Bowring Hospital Road, Shivajinagar, Bengaluru, Karnataka 560001 India
| |
Collapse
|
7924
|
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol 2018; 15:504-508. [PMID: 29402533 DOI: 10.1016/j.jacr.2017.12.026] [Citation(s) in RCA: 254] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/13/2022]
Abstract
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
Collapse
Affiliation(s)
- James H Thrall
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Cinthia Cruz
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Synho Do
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Keith Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - James Brink
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
7925
|
Abstract
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.
Collapse
Affiliation(s)
| | - Eric W Brown
- Foundational Innovations, IBM Watson Health, Yorktown Heights, NY
| |
Collapse
|
7926
|
Chenar SS, Deng Z. Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast. Environ Int 2018; 111:212-223. [PMID: 29232561 DOI: 10.1016/j.envint.2017.11.032] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 11/10/2017] [Accepted: 11/30/2017] [Indexed: 05/21/2023]
Abstract
This paper presents an artificial intelligence-based model, called ANN-2Day model, for forecasting, managing and ultimately eliminating the growing risk of oyster norovirus outbreaks. The ANN-2Day model was developed using Artificial Neural Network (ANN) Toolbox in MATLAB Program and 15-years of epidemiological and environmental data for six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall. It was found that oyster norovirus outbreaks can be forecasted with two-day lead time using the ANN-2Day model and daily data of the six environmental predictors. Forecasting results of the ANN-2Day model indicated that the model was capable of reproducing 19years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with the positive predictive value of 76.82%, the negative predictive value of 100.00%, the sensitivity of 100.00%, the specificity of 99.84%, and the overall accuracy of 99.83%, respectively, demonstrating the efficacy of the ANN-2Day model in predicting the risk of norovirus outbreaks to human health. The 2-day lead time enables public health agencies and oyster harvesters to plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents.
Collapse
Affiliation(s)
- Shima Shamkhali Chenar
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States.
| | - Zhiqiang Deng
- Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States.
| |
Collapse
|
7927
|
Abstract
Physicians in everyday clinical practice are under pressure to innovate faster than ever because of the rapid, exponential growth in healthcare data. "Big data" refers to extremely large data sets that cannot be analyzed or interpreted using traditional data processing methods. In fact, big data itself is meaningless, but processing it offers the promise of unlocking novel insights and accelerating breakthroughs in medicine-which in turn has the potential to transform current clinical practice. Physicians can analyze big data, but at present it requires a large amount of time and sophisticated analytic tools such as supercomputers. However, the rise of artificial intelligence (AI) in the era of big data could assist physicians in shortening processing times and improving the quality of patient care in clinical practice. This editorial provides a glimpse at the potential uses of AI technology in clinical practice and considers the possibility of AI replacing physicians, perhaps altogether. Physicians diagnose diseases based on personal medical histories, individual biomarkers, simple scores (e.g., CURB-65, MELD), and their physical examinations of individual patients. In contrast, AI can diagnose diseases based on a complex algorithm using hundreds of biomarkers, imaging results from millions of patients, aggregated published clinical research from PubMed, and thousands of physician's notes from electronic health records (EHRs). While AI could assist physicians in many ways, it is unlikely to replace physicians in the foreseeable future. Let us look at the emerging uses of AI in medicine.
Collapse
Affiliation(s)
- C Krittanawong
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai St' Luke and Mount Sinai West, New York, NY, United States.
| |
Collapse
|
7928
|
Bakkar N, Kovalik T, Lorenzini I, Spangler S, Lacoste A, Sponaugle K, Ferrante P, Argentinis E, Sattler R, Bowser R. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol 2018; 135:227-247. [PMID: 29134320 PMCID: PMC5773659 DOI: 10.1007/s00401-017-1785-8] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 11/04/2017] [Accepted: 11/04/2017] [Indexed: 12/12/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease with no effective treatments. Numerous RNA-binding proteins (RBPs) have been shown to be altered in ALS, with mutations in 11 RBPs causing familial forms of the disease, and 6 more RBPs showing abnormal expression/distribution in ALS albeit without any known mutations. RBP dysregulation is widely accepted as a contributing factor in ALS pathobiology. There are at least 1542 RBPs in the human genome; therefore, other unidentified RBPs may also be linked to the pathogenesis of ALS. We used IBM Watson® to sieve through all RBPs in the genome and identify new RBPs linked to ALS (ALS-RBPs). IBM Watson extracted features from published literature to create semantic similarities and identify new connections between entities of interest. IBM Watson analyzed all published abstracts of previously known ALS-RBPs, and applied that text-based knowledge to all RBPs in the genome, ranking them by semantic similarity to the known set. We then validated the Watson top-ten-ranked RBPs at the protein and RNA levels in tissues from ALS and non-neurological disease controls, as well as in patient-derived induced pluripotent stem cells. 5 RBPs previously unlinked to ALS, hnRNPU, Syncrip, RBMS3, Caprin-1 and NUPL2, showed significant alterations in ALS compared to controls. Overall, we successfully used IBM Watson to help identify additional RBPs altered in ALS, highlighting the use of artificial intelligence tools to accelerate scientific discovery in ALS and possibly other complex neurological disorders.
Collapse
Affiliation(s)
- Nadine Bakkar
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA
| | - Tina Kovalik
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA
| | - Ileana Lorenzini
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA
| | | | | | - Kyle Sponaugle
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA
| | - Philip Ferrante
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA
| | | | - Rita Sattler
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA
| | - Robert Bowser
- Department of Neurobiology, Barrow Neurological Institute, 350 W Thomas Road, Phoenix, AZ, 85013, USA.
| |
Collapse
|
7929
|
Fernandez-Luque L, Imran M. Humanitarian health computing using artificial intelligence and social media: A narrative literature review. Int J Med Inform 2018; 114:136-142. [PMID: 29395987 DOI: 10.1016/j.ijmedinf.2018.01.015] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 01/14/2018] [Accepted: 01/19/2018] [Indexed: 01/22/2023]
Abstract
INTRODUCTION According to the World Health Organization (WHO), over 130 million people are in constant need of humanitarian assistance due to natural disasters, disease outbreaks, and conflicts, among other factors. These health crises can compromise the resilience of healthcare systems, which are essential for achieving the health objectives of the sustainable development goals (SDGs) of the United Nations (UN). During a humanitarian health crisis, rapid and informed decision making is required. This is often challenging due to information scarcity, limited resources, and strict time constraints. Moreover, the traditional approach to digital health development, which involves a substantial requirement analysis, a feasibility study, and deployment of technology, is ill-suited for many crisis contexts. The emergence of Web 2.0 technologies and social media platforms in the past decade, such as Twitter, has created a new paradigm of massive information and misinformation, in which new technologies need to be developed to aid rapid decision making during humanitarian health crises. OBJECTIVE Humanitarian health crises increasingly require the analysis of massive amounts of information produced by different sources, such as social media content, and, hence, they are a prime case for the use of artificial intelligence (AI) techniques to help identify relevant information and make it actionable. To identify challenges and opportunities for using AI in humanitarian health crises, we reviewed the literature on the use of AI techniques to process social media. METHODOLOGY We performed a narrative literature review aimed at identifying examples of the use of AI in humanitarian health crises. Our search strategy was designed to get a broad overview of the different applications of AI in a humanitarian health crisis and their challenges. A total of 1459 articles were screened, and 24 articles were included in the final analysis. RESULTS Successful case studies of AI applications in a humanitarian health crisis have been reported, such as for outbreak detection. A commonly shared concern in the reviewed literature is the technical challenge of analyzing large amounts of data in real time. Data interoperability, which is essential to data sharing, is also a barrier with regard to the integration of online and traditional data sources. Human and organizational aspects that might be key factors for the adoption of AI and social media remain understudied. There is also a publication bias toward high-income countries, as we identified few examples in low-income countries. Further, we did not identify any examples of certain types of major crisis, such armed conflicts, in which misinformation might be more common. CONCLUSIONS The feasibility of using AI to extract valuable information during a humanitarian health crisis is proven in many cases. There is a lack of research on how to integrate the use of AI into the work-flow and large-scale deployments of humanitarian aid during a health crisis.
Collapse
|
7930
|
Cruz AS, Lins HC, Medeiros RVA, Filho JMF, da Silva SG. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomed Eng Online 2018; 17:12. [PMID: 29378578 PMCID: PMC5789692 DOI: 10.1186/s12938-018-0436-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 01/10/2018] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. METHODS A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms "Neural Network", "Osteoporosis Machine Learning" and "Osteoporosis Neural Network". Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000-2017 were selected; however, articles prior to this period with great relevance were included in this study. DISCUSSION Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. CONCLUSIONS It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.
Collapse
Affiliation(s)
- Agnaldo S. Cruz
- Centro de Tecnologia, Universidade Federal do Rio Grande do Norte UFRN, Av. Salgado Filho, Natal, Brazil
| | - Hertz C. Lins
- Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Ricardo V. A. Medeiros
- Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - José M. F. Filho
- Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Sandro G. da Silva
- Centro de Tecnologia, Universidade Federal do Rio Grande do Norte UFRN, Av. Salgado Filho, Natal, Brazil
| |
Collapse
|
7931
|
Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, Cruzado JM, Jonsson A. Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy. Kidney Dis (Basel) 2018; 4:1-9. [PMID: 29594137 DOI: 10.1159/000486394] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 12/14/2017] [Indexed: 12/14/2022]
Abstract
Background Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.
Collapse
Affiliation(s)
- Miguel Hueso
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Alfredo Vellido
- bIntelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Nuria Montero
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | | | - Rosa Ramos
- cFresenius Medical Care, Bad Homburg, Germany
| | - Manuel Angoso
- dDialysis Unit, Clínica Virgen del Consuelo, Valencia, Spain
| | - Josep Maria Cruzado
- aDepartment of Nephrology, Hospital Universitari Bellvitge, and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Anders Jonsson
- eArtificial Intelligence and Machine Learning Research Group, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| |
Collapse
|
7932
|
Miyake J, Kaneshita Y, Asatani S, Tagawa S, Niioka H, Hirano T. Graphical classification of DNA sequences of HLA alleles by deep learning. Hum Cell 2018; 31:102-5. [PMID: 29327117 DOI: 10.1007/s13577-017-0194-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Accepted: 11/22/2017] [Indexed: 12/03/2022]
Abstract
Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence “Deep Learning (Stacked autoencoder)”. Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimensional representation and were plotted. Profiles of the two-dimensional plots indicate that the alleles can be classified as clusters are formed. The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles.
Collapse
|
7933
|
Gisiger T, Boukadoum M. A loop-based neural architecture for structured behavior encoding and decoding. Neural Netw 2018; 98:318-336. [PMID: 29306756 DOI: 10.1016/j.neunet.2017.11.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 11/15/2017] [Accepted: 11/28/2017] [Indexed: 11/15/2022]
Abstract
We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research.
Collapse
Affiliation(s)
- Thomas Gisiger
- Centre for Research on Brain, Language and Music, 3640 de la Montagne, Montréal, Québec H3G 2A8, Canada.
| | - Mounir Boukadoum
- Département d'informatique, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal Québec H3C 3P8, Canada
| |
Collapse
|
7934
|
Dupoux E. Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner. Cognition 2018; 173:43-59. [PMID: 29324240 DOI: 10.1016/j.cognition.2017.11.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 11/10/2017] [Accepted: 11/22/2017] [Indexed: 10/18/2022]
Abstract
Spectacular progress in the information processing sciences (machine learning, wearable sensors) promises to revolutionize the study of cognitive development. Here, we analyse the conditions under which 'reverse engineering' language development, i.e., building an effective system that mimics infant's achievements, can contribute to our scientific understanding of early language development. We argue that, on the computational side, it is important to move from toy problems to the full complexity of the learning situation, and take as input as faithful reconstructions of the sensory signals available to infants as possible. On the data side, accessible but privacy-preserving repositories of home data have to be setup. On the psycholinguistic side, specific tests have to be constructed to benchmark humans and machines at different linguistic levels. We discuss the feasibility of this approach and present an overview of current results.
Collapse
|
7935
|
Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, Succi MD, Yun BJ. How artificial intelligence could transform emergency department operations. Am J Emerg Med 2018; 36:1515-1517. [PMID: 29321109 DOI: 10.1016/j.ajem.2018.01.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 01/03/2018] [Indexed: 12/20/2022] Open
Affiliation(s)
- Yosef Berlyand
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States
| | - Ali S Raja
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Stephen C Dorner
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Harvard Affiliated Emergency Medicine Residency Program, 5 Emerson Place, Suite 101, Boston, MA 02114, United States
| | - Anand M Prabhakar
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Jonathan D Sonis
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Ravi V Gottumukkala
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Marc David Succi
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Medically Engineered Solutions in Healthcare (MESH) Incubator, Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
| | - Brian J Yun
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States; Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States; Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States.
| |
Collapse
|
7936
|
Al Ajmi E, Forghani B, Reinhold C, Bayat M, Forghani R. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol 2018; 28:2604-11. [PMID: 29294157 DOI: 10.1007/s00330-017-5214-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 11/18/2017] [Accepted: 11/24/2017] [Indexed: 12/13/2022]
Abstract
OBJECTIVE There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. METHODS Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. RESULTS Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. CONCLUSIONS Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. KEY POINTS • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.
Collapse
|
7937
|
Houben JJ. [Recent advances in digestive surgery]. Rev Med Brux 2018; 39:359-364. [PMID: 30321001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
INTRODUCTION Surgical practice in gastro-enterology is concerned by deep technological advances. In the past century, the technological advances were conducted by clinical challenges and strategies. The 21th century is clearly led by the inversion of the paradigms. Medical practice does not only depend on the access to the technologies, but it seems submitted to her. Should the physician follow the engineer ? Does the clinical data collection, depend on the computer ? Who decides ? The doctor, the patient or the Artificial Intelligence ? Materiel et Methods : The present essay that definitely does not answer all these questions, is achieved thanks the practical experience of our colleagues. We also collected the recent literature devoted to new and promising technologies. The PUBMED review is completed by several think tanks reports coming from the industry. RESULTS Among the multiple aspects of present and future progresses, 6 among them could be pointed out: the benefit of augmented reality, mini and micro invasive techniques, robotic, news energies, big data and artificial intelligence. CONCLUSIONS Progresses in data collection and treatment, imminent advances in micro mechanics, will completely change our clinical practice. The role of the doctor is in the center of this approach. We have to prepare young people to this human revolution.
Collapse
Affiliation(s)
- J J Houben
- Service de Chirurgie digestive, Hôpital Erasme, ULB
| |
Collapse
|
7938
|
Howell LP. Papanicolaou Address: Why the next generation should take this journey and overcome constraint. J Am Soc Cytopathol 2018; 7:205-11. [PMID: 31043278 DOI: 10.1016/j.jasc.2018.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 03/12/2018] [Accepted: 03/26/2018] [Indexed: 11/21/2022]
Abstract
Cytopathology is experiencing many forces that are changing and constraining current practice, including the need for cost efficiencies, new technologies, expectations for higher quality and faster turnaround time, and a diminishing workforce. Two "hot topics" that will have considerable influence on the changes in the future practice of cytopathology are artificial intelligence and optimization of cervical screening intervals and methods. The future growth and success of the cytopathology subspecialty will require using constraint as a catalyst to achieve transformative solutions, as well as an optimistic "we can if…" entrepreneurial attitude. Success will also require living the field's traditions and values: mentorship, sponsorship, innovation and creativity, a willingness to assume new roles, and the ability to network and support career journeys through active participation in a professional society.
Collapse
|
7939
|
Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F, Schafer B, Valcke P, Vayena E. AI4People-An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds Mach (Dordr) 2018. [PMID: 30930541 DOI: 10.1093/hmg/ddy137/4972370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
This article reports the findings of AI4People, an Atomium-EISMD initiative designed to lay the foundations for a "Good AI Society". We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations-to assess, to develop, to incentivise, and to support good AI-which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.
Collapse
Affiliation(s)
- Luciano Floridi
- 1Oxford Internet Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Josh Cowls
- 1Oxford Internet Institute, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | | | - Raja Chatila
- 4French National Center of Scientific Research, Paris, France
- 5Institute of Intelligent Systems and Robotics, Pierre and Marie Curie University, Paris, France
| | | | - Virginia Dignum
- 7University of Umeå, Umeå, Sweden
- 8Delft Design for Values Institute, Delft University of Technology, Delft, The Netherlands
| | - Christoph Luetge
- 9TUM School of Governance, Technical University of Munich, Munich, Germany
| | - Robert Madelin
- 10Centre for Technology and Global Affairs, University of Oxford, Oxford, UK
| | - Ugo Pagallo
- 11Department of Law, University of Turin, Turin, Italy
| | - Francesca Rossi
- 12IBM Research, New York, USA
- 13University of Padova, Padua, Italy
| | | | - Peggy Valcke
- 15Centre for IT & IP Law, Catholic University of Leuven, Flanders, Belgium
- 16Bocconi University, Milan, Italy
| | - Effy Vayena
- 17Bioethics, Health Ethics and Policy Lab, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
7940
|
Abstract
In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihypertensive medication and intradialytic symptoms in children whose target weight was prescribed firstly by a nephrologist, then subsequently using a machine learning algorithm. Improvements in all outcome measures are reported. Their innovative approach to tackling this important clinical problem appears promising. In this editorial, we discuss the strengths and weaknesses of their study and consider to what extent machine learning strategies are suited to optimizing pediatric dialysis outcomes.
Collapse
Affiliation(s)
- Wesley Hayes
- Great Ormond Street Hospital, London, UK. .,University College London Institute of Child Health, London, UK.
| | | |
Collapse
|
7941
|
Kiral-Kornek I, Roy S, Nurse E, Mashford B, Karoly P, Carroll T, Payne D, Saha S, Baldassano S, O'Brien T, Grayden D, Cook M, Freestone D, Harrer S. Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System. EBioMedicine 2018; 27:103-111. [PMID: 29262989 PMCID: PMC5828366 DOI: 10.1016/j.ebiom.2017.11.032] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/16/2017] [Accepted: 11/28/2017] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. METHODS Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. RESULTS The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. CONCLUSION This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance.
Collapse
Affiliation(s)
| | - Subhrajit Roy
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia
| | - Ewan Nurse
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia; The University of Melbourne, 3010 Parkville, VIC, Australia
| | - Benjamin Mashford
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia
| | - Philippa Karoly
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia; The University of Melbourne, 3010 Parkville, VIC, Australia
| | - Thomas Carroll
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia; The University of Melbourne, 3010 Parkville, VIC, Australia
| | - Daniel Payne
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia; The University of Melbourne, 3010 Parkville, VIC, Australia
| | - Susmita Saha
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia
| | | | | | - David Grayden
- Department of Biomedical Engineering, The University of Melbourne, 3010 Parkville, VIC, Australia
| | - Mark Cook
- The University of Melbourne, 3010 Parkville, VIC, Australia
| | - Dean Freestone
- The University of Melbourne, 3010 Parkville, VIC, Australia.
| | - Stefan Harrer
- IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia.
| |
Collapse
|
7942
|
Senders JT, Zaki MM, Karhade AV, Chang B, Gormley WB, Broekman ML, Smith TR, Arnaout O. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien) 2018; 160:29-38. [PMID: 29134342 DOI: 10.1007/s00701-017-3385-8] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 10/29/2017] [Indexed: 12/20/2022]
Abstract
BACKGROUND Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care. METHOD A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment. RESULTS Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction. CONCLUSIONS ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.
Collapse
|
7943
|
Abstract
Future medical technology breakthroughs will build from the incredible progress made in computers, biotechnology, and nanotechnology and from the information learned from the human genome. With such technology and information, computer-aided diagnoses, organ replacement, gene therapy, personalized drugs, and even age reversal will become possible. True 3-dimensional system technology will enable surgeons to envision key clinical features and will help them in planning complex surgery. Surgeons will enter surgical instructions in a virtual space from a remote medical center, order a medical robot to perform the operation, and review the operation in real time on a monitor. Surgeons will be better than artificial intelligence or automated robots when surgeons (or we) love patients and ask questions for a better future. The purpose of this paper is looking at the future medical science and the changes of colorectal surgeons.
Collapse
Affiliation(s)
- Young Jin Kim
- Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| |
Collapse
|
7944
|
Abstract
Next generation sequencing (NGS) of cancer genomes is now becoming a prerequisite for accurate diagnosis and proper treatment in clinical oncology. Because the genomic regions for NGS expand from a certain set of genes to the whole exome or whole genome, the resulting sequence data becomes incredibly enormous and makes it quite laborious to translate the genomic data into medicine, so-called annotation and curation. We organized a clinical sequencing team and established a bidirectional (bed-to-bench and bench-to-bed) system to integrate clinical and genomic data for hematological malignancies. We also started a collaborative research project with IBM Japan to adopt the artificial intelligence Watson for Genomics (WfG) to the pipeline of medical informatics. Genomic DNA was prepared from malignant as well as normal tissues in each patient and subjected to NGS. Sequence data was analyzed using an in-house semi-automated pipeline in combination with WfG, which was used to identify candidate driver mutations and relevant pathways from which applicable drug information was deduced. Currently, we have analyzed more than 150 patients with hematological disorders, including AML and ALL, and obtained many informative findings. In this presentation, I will introduce some of the achievements we have made so far.
Collapse
Affiliation(s)
- Arinobu Tojo
- Division of Molecular Therapy, Advanced Clinical Research Center, The Institute of Medical Science, The University of Tokyo
| |
Collapse
|
7945
|
Komeda Y, Handa H, Watanabe T, Nomura T, Kitahashi M, Sakurai T, Okamoto A, Minami T, Kono M, Arizumi T, Takenaka M, Hagiwara S, Matsui S, Nishida N, Kashida H, Kudo M. Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience. Oncology 2017; 93 Suppl 1:30-34. [PMID: 29258081 DOI: 10.1159/000481227] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIM Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. METHODS A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. RESULTS The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. CONCLUSION A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy.
Collapse
Affiliation(s)
- Yoriaki Komeda
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka-Sayama, Japan
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
7946
|
Kong X, Gong S, Su L, Howard N, Kong Y. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods. EBioMedicine 2018; 27:94-102. [PMID: 29269039 DOI: 10.1016/j.ebiom.2017.12.015] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 12/06/2017] [Accepted: 12/14/2017] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. METHODS In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. RESULTS The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. CONCLUSIONS Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity.
Collapse
|
7947
|
Jimenez-Romero C, Johnson J. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo. Neural Comput Appl 2017; 28:755-64. [PMID: 29213189 DOI: 10.1007/s00521-016-2398-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 05/25/2016] [Indexed: 10/25/2022]
Abstract
The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.
Collapse
|
7948
|
Bzdok D, Meyer-Lindenberg A. Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol Psychiatry Cogn Neurosci Neuroimaging 2017; 3:223-230. [PMID: 29486863 DOI: 10.1016/j.bpsc.2017.11.007] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 11/17/2017] [Indexed: 12/17/2022]
Abstract
The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, cross-validation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patients can better predict treatment outcomes than DSM/ICD diagnoses can. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection, and dosage adjustment to reduce the burden of disease. This primer aims to introduce clinicians and researchers to the opportunities and challenges in bringing machine intelligence into psychiatric practice.
Collapse
Affiliation(s)
- Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Aachen, Germany; Parietal team, INRIA, Neurospin, Gif-sur-Yvette, France.
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| |
Collapse
|
7949
|
Nakajima K, Kudo T, Nakata T, Kiso K, Kasai T, Taniguchi Y, Matsuo S, Momose M, Nakagawa M, Sarai M, Hida S, Tanaka H, Yokoyama K, Okuda K, Edenbrandt L. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study. Eur J Nucl Med Mol Imaging 2017; 44:2280-2289. [PMID: 28948350 PMCID: PMC5680364 DOI: 10.1007/s00259-017-3834-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Accepted: 09/06/2017] [Indexed: 01/13/2023]
Abstract
PURPOSE Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. METHODS The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest 99mTc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. RESULTS The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were >0.80. CONCLUSION The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease.
Collapse
Affiliation(s)
| | | | | | - Keisuke Kiso
- National Cerebral and Cardiovascular Center, Suita, Japan
| | - Tokuo Kasai
- Tokyo Medical University Hachioji Medical Center, Hachioji, Japan
| | | | | | | | | | | | | | - Hirokazu Tanaka
- Tokyo Medical University Ibaraki Medical Center, Ibaraki, Japan
| | | | - Koichi Okuda
- Kanazawa Medical University, Uchinada, Kahoku, Japan
| | | |
Collapse
|
7950
|
Patel NM, Michelini VV, Snell JM, Balu S, Hoyle AP, Parker JS, Hayward MC, Eberhard DA, Salazar AH, McNeillie P, Xu J, Huettner CS, Koyama T, Utro F, Rhrissorrakrai K, Norel R, Bilal E, Royyuru A, Parida L, Earp HS, Grilley-Olson JE, Hayes DN, Harvey SJ, Sharpless NE, Kim WY. Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing. Oncologist 2017; 23:179-185. [PMID: 29158372 PMCID: PMC5813753 DOI: 10.1634/theoncologist.2017-0170] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 10/06/2017] [Indexed: 11/20/2022] Open
Abstract
Next‐generation sequencing (NGS) has emerged as an affordable and reproducible means to query tumors for somatic genetic anomalies. To help interpret somatic NGS data, many institutions have created a molecular tumor board to analyze the results of NGS and make recommendations. This article evaluates the utility of cognitive computing systems to analyze data for clinical decision‐making. Background. Using next‐generation sequencing (NGS) to guide cancer therapy has created challenges in analyzing and reporting large volumes of genomic data to patients and caregivers. Specifically, providing current, accurate information on newly approved therapies and open clinical trials requires considerable manual curation performed mainly by human “molecular tumor boards” (MTBs). The purpose of this study was to determine the utility of cognitive computing as performed by Watson for Genomics (WfG) compared with a human MTB. Materials and Methods. One thousand eighteen patient cases that previously underwent targeted exon sequencing at the University of North Carolina (UNC) and subsequent analysis by the UNCseq informatics pipeline and the UNC MTB between November 7, 2011, and May 12, 2015, were analyzed with WfG, a cognitive computing technology for genomic analysis. Results. Using a WfG‐curated actionable gene list, we identified additional genomic events of potential significance (not discovered by traditional MTB curation) in 323 (32%) patients. The majority of these additional genomic events were considered actionable based upon their ability to qualify patients for biomarker‐selected clinical trials. Indeed, the opening of a relevant clinical trial within 1 month prior to WfG analysis provided the rationale for identification of a new actionable event in nearly a quarter of the 323 patients. This automated analysis took <3 minutes per case. Conclusion. These results demonstrate that the interpretation and actionability of somatic NGS results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing could potentially improve patient care by providing a rapid, comprehensive approach for data analysis and consideration of up‐to‐date availability of clinical trials. Implications for Practice. The results of this study demonstrate that the interpretation and actionability of somatic next‐generation sequencing results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing can significantly improve patient care by providing a fast, cost‐effective, and comprehensive approach for data analysis in the delivery of precision medicine. Patients and physicians who are considering enrollment in clinical trials may benefit from the support of such tools applied to genomic data.
Collapse
Affiliation(s)
- Nirali M Patel
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Jeff M Snell
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Saianand Balu
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alan P Hoyle
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Joel S Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Michele C Hayward
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - David A Eberhard
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ashley H Salazar
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Jia Xu
- IBM Watson Health, Cambridge, Massachusetts, USA
| | | | | | | | | | | | - Erhan Bilal
- IBM Research, Yorktown Heights, New York, USA
| | | | | | - H Shelton Earp
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Juneko E Grilley-Olson
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - D Neil Hayes
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Norman E Sharpless
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - William Y Kim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| |
Collapse
|