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Machine learning surgery

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https://www.readbyqxmd.com/read/28227123/non-invasive-optical-imaging-techniques-for-burn-injured-tissue-detection-for-debridement-surgery
#1
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
#2
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
#3
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
#4
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...
January 11, 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
#5
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])...
January 25, 2017: Spine
https://www.readbyqxmd.com/read/28113295/using-contact-forces-and-robot-arm-accelerations-to-automatically-rate-surgeon-skill-at-peg-transfer
#6
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
#7
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
#8
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
#9
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
#10
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...
October 5, 2016: Journal of Surgical Research
https://www.readbyqxmd.com/read/28026747/joint-dictionary-learning-based-non-negative-matrix-factorization-for-voice-conversion-to-improve-speech-intelligibility-after-oral-surgery
#11
Szu-Wei Fu, Pei-Chun Li, Ying-Hui Lai, Cheng-Chien Yang, Li-Chun Hsieh, Yu Tsao
OBJECTIVE: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: (1) the amount of training data may be limited (because speaking for a long time is usually difficult for post-operative patients); (2) rapid conversion is desirable (for better communication)...
December 23, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28018557/a-hybrid-computer-aided-diagnosis-system-for-prediction-of-breast-cancer-recurrence-hpbcr-using-optimized-ensemble-learning
#12
Mohammad R Mohebian, Hamid R Marateb, Marjan Mansourian, Miguel Angel Mañanas, Fariborz Mokarian
Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT)...
2017: Computational and Structural Biotechnology Journal
https://www.readbyqxmd.com/read/27989606/improving-diagnostic-recognition-of-primary-hyperparathyroidism-with-machine-learning
#13
Yash R Somnay, Mark Craven, Kelly L McCoy, Sally E Carty, Tracy S Wang, Caprice C Greenberg, David F Schneider
BACKGROUND: Parathyroidectomy offers the only cure for primary hyperparathyroidism, but today only 50% of primary hyperparathyroidism patients are referred for operation, in large part, because the condition is widely under-recognized. The diagnosis of primary hyperparathyroidism can be especially challenging with mild biochemical indices. Machine learning is a collection of methods in which computers build predictive algorithms based on labeled examples. With the aim of facilitating diagnosis, we tested the ability of machine learning to distinguish primary hyperparathyroidism from normal physiology using clinical and laboratory data...
December 15, 2016: Surgery
https://www.readbyqxmd.com/read/27925585/using-contact-forces-and-robot-arm-accelerations-to-automatically-rate-surgeon-skill-at-peg-transfer
#14
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/27882725/analysis-of-risk-factors-to-predict-communicating-hydrocephalus-following-gamma-knife-radiosurgery-for-intracranial-schwannoma
#15
Seunghoon Lee, Seong-Wook Seo, Juyoung Hwang, Ho Jun Seol, Do-Hyun Nam, Jung-Il Lee, Doo-Sik Kong
Communicating hydrocephalus (HCP) in vestibular schwannomas (VS) after gamma knife radiosurgery (GKRS) has been reported in the literature. However, little information about its incidence and risk factors after GKRS for intracranial schwannomas is yet available. The objective of this study was to identify the incidence and risk factors for developing communicating HCP after GKRS for intracranial schwannomas. We retrospectively reviewed a total of 702 patients with intracranial schwannomas who were treated with GKRS between January 2002 and December 2015...
December 2016: Cancer Medicine
https://www.readbyqxmd.com/read/27798643/predictors-of-post-operative-mycetoma-recurrence-using-machine-learning-algorithms-the-mycetoma-research-center-experience
#16
Ali Wadal, Tusneem Ahmed Elhassan, Hajer Ahmed Zein, Manar Elsheikh Abdel-Rahman, Ahmed Hassan Fahal
Post-operative recurrence in mycetoma after adequate medical and surgical treatment is common and a serious problem. It has health, socio-economic and psychological detrimental effects on patients and families. It is with this in mind, we set out to determine the predictors of post-operative recurrence in mycetoma. The study included 1013 patients with Madurella mycetomatis causing eumycetoma who underwent surgical excision at the Mycetoma Research Centre, Khartoum, Sudan in the period 1991-2015. The clinical records of these patients were reviewed and relevant information was collected using a pre-designed data collection sheet...
October 2016: PLoS Neglected Tropical Diseases
https://www.readbyqxmd.com/read/27740698/a-practical-decision-tree-model-to-predict-complexity-of-reconstructive-surgery-after-periocular-basal-cell-carcinoma-excision
#17
E Tan, F Lin, L Sheck, P Salmon, S Ng
PURPOSE: To derive a decision rule for predicting surgical complexity in periorbital basal cell carcinoma (pBCC). DESIGN: Prospective, cohort study. PARTICIPANTS: Patients referred to an oculoplastic service for excision of pBCC from September 2010 to November 2015 METHODS: This study was conducted at two centres in New Zealand from September 2010 to November 2015. Baseline demographic information, and an initial assessment of operative complexity (a four-point grading scale) were collected...
October 14, 2016: Journal of the European Academy of Dermatology and Venereology: JEADV
https://www.readbyqxmd.com/read/27713065/using-intraoperative-ultrasonography-for-spinal-cord-tumor-surgery
#18
Marcel Ivanov, Alexandru Budu, Hugh Sims-Williams, Ion Poeata
BACKGROUND: Our aim was to evaluate the usefulness of modern intraoperative ultrasonography (iUS) in the resection of a wide variety of spinal intradural pathologic entities. METHODS: We evaluated patients with spinal cord disease treated between January 2006 and September 2015. Intraoperative standard B-mode images were acquired using a 3.5-MHz to 12-MHz ultrasonographic probes (linear and curvilinear) on various ultrasound machines. The benefits and disadvantages of iUS were assessed for each case...
January 2017: World Neurosurgery
https://www.readbyqxmd.com/read/27709666/stop-border-ahead-automatic-detection-of-subthalamic-exit-during-deep-brain-stimulation-surgery
#19
Dan Valsky, Odeya Marmor-Levin, Marc Deffains, Renana Eitan, Kim T Blackwell, Hagai Bergman, Zvi Israel
BACKGROUND: Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the STN borders is performed manually by a neurophysiologist. The exact detection of the borders is difficult, especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to suboptimal location of the DBS lead and inadequate clinical outcomes...
January 2017: Movement Disorders: Official Journal of the Movement Disorder Society
https://www.readbyqxmd.com/read/27693769/outcomes-and-complications-following-endovascular-treatment-of-brain-arteriovenous-malformations-a-prognostication-attempt-using-artificial-intelligence
#20
Hamed Asadi, Hong Kuan Kok, Seamus Looby, Paul Brennan, Alan O'Hare, John Thornton
PURPOSE: This study aims to identify factors influencing outcome in brain arteriovenous malformations (BAVM) treated with endovascular embolisation. We also assessed the feasibility of using machine learning techniques to prognosticate and predict outcome and compared this to conventional statistical analyses. METHODS: A retrospective study of patients undergoing endovascular treatment of BAVM over a 22-year period in a national neuroscience centre was performed...
September 28, 2016: World Neurosurgery
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