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Machine learning surgery

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https://www.readbyqxmd.com/read/28521616/a-10-gene-classifier-for-indeterminate-thyroid-nodules-development-and-multicenter-accuracy-study
#1
Hernan E Gonzalez, Jose R Martínez, Sergio Vargas, Antonieta Solar, Loreto Pamela Véliz, Francisco Cruz, Tatiana Arias, Soledad Loyola, Eleonora Horvath, Hernán Tala, Eufrosina Traipe, Manuel Meneses, Luis Marin, Nelson Wohllk, Rene Eduardo Diaz, Jesús Véliz, Pedro Pineda, Patricia Arroyo, Natalia Mena, Milagros Bracamonte, Giovanna Miranda, Elsa Bruce, Soledad Urra
BACKGROUND: In most of the world, diagnostic surgery remains as the most frequent approach for indeterminate thyroid cytology. Although several molecular tests are available for central-lab testing in the US, there are no available kits for local laboratory testing. The aim of this study was to develop a prototype in-vitro diagnostic (IVD) gene classifier for diagnosis of indeterminate thyroid cytology. METHODS: In a first stage, the expression of 18 genes was determined by qPCR in a broad histopathological spectrum of fresh tissue biopsies (114)...
May 18, 2017: Thyroid: Official Journal of the American Thyroid Association
https://www.readbyqxmd.com/read/28516300/predicting-surgical-skill-from-the-first-n-seconds-of-a-task-value-over-task-time-using-the-isogony-principle
#2
Anna French, Thomas S Lendvay, Robert M Sweet, Timothy M Kowalewski
PURPOSE: Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring critical inefficiencies or skill deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant surgical tasks or steps during robot-assisted surgery. METHODS: Features calculated from the isogony principle are used to train four common machine learning algorithms from dry-lab laparoscopic data gathered from three common training exercises...
May 17, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28497663/mortality-risk-prediction-models-for-coronary-artery-bypass-graft-surgery-current-scenario-and-future-direction
#3
Md N Karim, Christopher M Reid, Andrew Cochrane, Lavinia Tran, Mohammed Alramadan, Md N Hossain, Baki Billah
INTRODUCTION: Many risk prediction models are currently in use for predicting short-term mortality following Coronary Artery Bypass Graft (CABG) surgery. This review critically appraised the methods that were used for developing these models to assess their applicability in current practice setting as well as for the necessity of up-gradation. EVIDENCE ACQUISITION: Medline via Ovid was searched for articles published between 1946 and 2016 and EMBASE via Ovid between 1974 and 2016 to identify risk prediction models for CABG...
May 11, 2017: Journal of Cardiovascular Surgery
https://www.readbyqxmd.com/read/28483984/machine-learning-to-support-decision-making-for-cardiac-surgery-during-the-acute-phase-of-infective-endocarditis
#4
EDITORIAL
Erwan Donal, Erwan Flecher, Pierre Tattevin
No abstract text is available yet for this article.
May 8, 2017: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/28434153/machine-learning-xgboost-analysis-of-language-networks-to-classify-patients-with-epilepsy
#5
L Torlay, M Perrone-Bertolotti, E Thomas, M Baciu
Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing 'atypical' (compared to 'typical' in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures...
April 22, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28368992/validation-of-an-objective-keratoconus-detection-system-implemented-in-a-scheimpflug-tomographer-and-comparison-with-other-methods
#6
Irene Ruiz Hidalgo, Jos J Rozema, Alain Saad, Damien Gatinel, Pablo Rodriguez, Nadia Zakaria, Carina Koppen
PURPOSE: To validate a recently developed program for automatic and objective keratoconus detection (Keratoconus Assistant [KA]) by applying it to a new population and comparing it with other methods described in the literature. METHODS: KA uses machine learning and 25 Pentacam-derived parameters to classify eyes into subgroups, such as keratoconus, keratoconus suspect, postrefractive surgery, and normal eyes. To validate this program, it was applied to 131 eyes diagnosed separately by experienced corneal specialists from 2 different centers (Fondation Rothschild, Paris, and Antwerp University Hospital [UZA])...
June 2017: Cornea
https://www.readbyqxmd.com/read/28365240/using-electronic-health-records-to-build-an-ophthalmological-data-warehouse-and-visualize-patients-data
#7
Karsten U Kortüm, Michael Müller, Christoph Kern, Alexander Babenko, Wolfgang J Mayer, Anselm Kampik, Thomas C Kreutzer, Siegfried Priglinger, Christoph Hirneiss
PURPOSE: To develop a near real-time data warehouse (DW) in an academic ophthalmological center to gain scientific use of increasing digital data from electronic medical records (EMR) and diagnostic devices. Design; Database development METHODS: Specific macular clinic user interfaces within the institutional hospital information system were created. Orders for imaging modalities were sent by an EMR -linked picture-archiving and communications system to the respective devices. All data of 325,767 patients since 2002 were gathered in a DW running on a SQL database...
March 29, 2017: American Journal of Ophthalmology
https://www.readbyqxmd.com/read/28333183/machine-learning-algorithms-for-objective-remission-and-clinical-outcomes-with-thiopurines
#8
Akbar K Waljee, Kay Sauder, Anand Patel, Sandeep Segar, Boang Liu, Yiwei Zhang, Ji Zhu, Ryan W Stidham, Ulysses Balis, Peter D R Higgins
Background and Aims: Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR]. Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year...
March 14, 2017: Journal of Crohn's & Colitis
https://www.readbyqxmd.com/read/28319476/anesthesia-in-patients-with-infectious-disease-caused-by-multi-drug-resistant-bacteria
#9
Sharon Einav, Yonit Wiener-Well
PURPOSE OF REVIEW: Up to 50% of specific bacterial strains in healthcare admission facilities are multi-drug resistant organisms (MDROs). Involvement of anesthesiologists in management of patients carrying/at risk of carrying MDROs may decrease transmission in the Operating Room (OR). RECENT FINDINGS: Anesthesiologists, their work area and tools have all been implicated in MDRO outbreaks. Causes include contamination of external ventilation circuits and noncontribution of filters to prevention, inappropriate decontamination procedures for nondisposable equipment (e...
June 2017: Current Opinion in Anaesthesiology
https://www.readbyqxmd.com/read/28268919/non-invasive-optical-imaging-techniques-for-burn-injured-tissue-detection-for-debridement-surgery
#10
Juan Heredia-Juesas, Jeffrey E Thatcher, Yang Lu, John J Squiers, Darlene King, Wensheng Fan, J Michael DiMaio, Jose A Martinez-Lorenzo
Burn debridement is a challenging technique that requires significant skill to identify regions requiring excision and appropriate excision depth. A machine learning tool is being developed in order to assist surgeons by providing a quantitative assessment of burn-injured tissue. Three noninvasive optical imaging techniques capable of distinguishing between four kinds of tissue-healthy skin, viable wound bed, deep burn, and shallow burn-during serial burn debridement in a porcine model are presented in this paper...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227123/non-invasive-optical-imaging-techniques-for-burn-injured-tissue-detection-for-debridement-surgery
#11
Juan Heredia-Juesas, Jeffrey E Thatcher, Yang Lu, John J Squiers, Darlene King, Wensheng Fan, J Michael DiMaio, Jose A Martinez-Lorenzo, Juan Heredia-Juesas, Jeffrey E Thatcher, Yang Lu, John J Squiers, Darlene King, Wensheng Fan, J Michael DiMaio, Jose A Martinez-Lorenzo, Jeffrey E Thatcher, Darlene King, Jose A Martinez-Lorenzo, Wensheng Fan, Juan Heredia-Juesas, Yang Lu, John J Squiers
Burn debridement is a challenging technique that requires significant skill to identify regions requiring excision and appropriate excision depth. A machine learning tool is being developed in order to assist surgeons by providing a quantitative assessment of burn-injured tissue. Three noninvasive optical imaging techniques capable of distinguishing between four kinds of tissue-healthy skin, viable wound bed, deep burn, and shallow burn-during serial burn debridement in a porcine model are presented in this paper...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28166392/interictal-network-synchrony-and-local-heterogeneity-predict-epilepsy-surgery-outcome-among-pediatric-patients
#12
Samuel B Tomlinson, Brenda E Porter, Eric D Marsh
OBJECTIVE: Epilepsy is a disorder of aberrant cortical networks. Researchers have proposed that characterizing presurgical network connectivity may improve the surgical management of intractable seizures, but few studies have rigorously examined the relationship between network activity and surgical outcome. In this study, we assessed whether local and global measures of network activity differentiated patients with favorable (seizure-free) versus unfavorable (seizure-persistent) surgical outcomes...
February 6, 2017: Epilepsia
https://www.readbyqxmd.com/read/28155719/contralateral-artery-enlargement-predicts-carotid-plaque-progression-based-on-machine-learning-algorithm-models-in-apoe-mice
#13
Bing Li, Yun Jiao, Cong Fu, Bo Xie, Genshan Ma, Gaojun Teng, Yuyu Yao
BACKGROUND: This study specifically focused on anatomical MRI characterization of the low shear stress-induced atherosclerotic plaque in mice. We used machine learning algorithms to analyze multiple correlation factors of plaque to generate predictive models and to find the predictive factor for vulnerable plaque. METHODS: Branches of the left carotid artery in apoE(-/-) and C57BL/6J mice were ligated to produce the partial left carotid artery model. Before surgery, and 7, 14, and 28 days after surgery, in vivo serial MRI measurements of carotid artery diameter were obtained...
December 28, 2016: Biomedical Engineering Online
https://www.readbyqxmd.com/read/28126242/artificial-intelligence-in-medicine
#14
REVIEW
Pavel Hamet, Johanne Tremblay
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation...
April 2017: Metabolism: Clinical and Experimental
https://www.readbyqxmd.com/read/28125523/machine-learning-based-classification-of-38-years-of-spine-related-literature-into-100-research-topics
#15
David C Sing, Lionel N Metz, Stefan Dudli
STUDY DESIGN: Retrospective review. OBJECTIVE: To identify the top 100 spine research topics. SUMMARY OF BACKGROUND DATA: Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends. METHODS: Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS])...
June 1, 2017: Spine
https://www.readbyqxmd.com/read/28113295/using-contact-forces-and-robot-arm-accelerations-to-automatically-rate-surgeon-skill-at-peg-transfer
#16
Jeremy Brown, Conor O'Brien, Sarah Leung, Kristoffel Dumon, David Lee, Katherine Kuchenbecker
OBJECTIVE: Most trainees begin learning robotic minimally invasive surgery by performing inanimate practice tasks with clinical robots such as the Intuitive Surgical da Vinci. Expert surgeons are commonly asked to evaluate these performances using standardized five-point rating scales, but doing such ratings is time consuming, tedious, and somewhat subjective. This article presents an automatic skill evaluation system that analyzes only the contact force with the task materials, the broad-bandwidth accelerations of the robotic instruments and camera, and the task completion time...
December 2, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28060903/a-comparison-of-a-machine-learning-model-with-euroscore-ii-in-predicting-mortality-after-elective-cardiac-surgery-a-decision-curve-analysis
#17
Jérôme Allyn, Nicolas Allou, Pascal Augustin, Ivan Philip, Olivier Martinet, Myriem Belghiti, Sophie Provenchere, Philippe Montravers, Cyril Ferdynus
BACKGROUND: The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. METHODS AND FINDING: We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA...
2017: PloS One
https://www.readbyqxmd.com/read/28046226/su-f-r-22-malignancy-classification-for-small-pulmonary-nodules-with-radiomics-and-logistic-regression
#18
W Huang, S Tu
PURPOSE: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. METHODS: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28043064/electrophysiological-resting-state-biomarker-for-diagnosing-mesial-temporal-lobe-epilepsy-with-hippocampal-sclerosis
#19
Seung-Hyun Jin, Chun Kee Chung
The main aim of the present study was to evaluate whether resting-state functional connectivity of magnetoencephalography (MEG) signals can differentiate patients with mesial temporal lobe epilepsy (MTLE) from healthy controls (HC) and can differentiate between right and left MTLE as a diagnostic biomarker. To this end, a support vector machine (SVM) method among various machine learning algorithms was employed. We compared resting-state functional networks between 46 MTLE (right MTLE=23; left MTLE=23) patients with histologically proven HS who were free of seizure after surgery, and 46 HC...
November 23, 2016: Epilepsy Research
https://www.readbyqxmd.com/read/28032554/detection-of-clinically-important-colorectal-surgical-site-infection-using-bayesian-network
#20
Sunghwan Sohn, David W Larson, Elizabeth B Habermann, James M Naessens, Jasim Y Alabbad, Hongfang Liu
BACKGROUND: Despite extensive efforts to monitor and prevent surgical site infections (SSIs), real-time surveillance of clinical practice has been sparse and expensive or nonexistent. However, natural language processing (NLP) and machine learning (i.e., Bayesian network analysis) may provide the methodology necessary to approach this issue in a new way. We investigated the ability to identify SSIs after colorectal surgery (CRS) through an automated detection system using a Bayesian network...
March 2017: Journal of Surgical Research
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