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Machine learning surgery

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
Marcel Ivanov, Alexandru Budu, Hugh Sims-Williams, Ion Poeata
BACKGROUND: Our aim was to evaluate the utility of modern intraoperative ultrasound in the resection of a wide variety of spinal intradural pathological entities. MATERIALS AND METHODS: The authors evaluated patients with spinal cord pathology treated between January 2006 and September 2015. Intraoperative standard B-mode images were acquired using a 3.5-12 MHz US probes (linear and curvilinear) on various ultrasound machines. The benefits and disadvantages of intraoperative ultrasound (iUS) were assessed for each case...
October 3, 2016: World Neurosurgery
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...
October 6, 2016: Movement Disorders: Official Journal of the Movement Disorder Society
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
Wenjun Xu, Jie Chen, Henry Y K Lau, Hongliang Ren
BACKGROUND: Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. METHODS: To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics...
September 20, 2016: International Journal of Medical Robotics + Computer Assisted Surgery: MRCAS
Ligang Luo, Liping Li, Jiajia Hu, Xiaozhe Wang, Boulin Hou, Tianze Zhang, Lue Ping Zhao
BACKGROUND: Healthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically...
2016: BMC Medical Informatics and Decision Making
M Gram, J Erlenwein, F Petzke, D Falla, M Przemeck, M I Emons, M Reuster, S S Olesen, A M Drewes
BACKGROUND: Opioids are often used for pain treatment, but the response is often insufficient and dependent on e.g. the pain condition, genetic factors and drug class. Thus, there is an urgent need to identify biomarkers to enable selection of the appropriate drug for the individual patient, a concept known as personalized medicine. Quantitative sensory testing (QST) and clinical parameters can provide some guidance for response, but better and more objective biomarkers are urgently warranted...
July 29, 2016: European Journal of Pain: EJP
Ana Monsalve-Torra, Daniel Ruiz-Fernandez, Oscar Marin-Alonso, Antonio Soriano-Payá, Jaime Camacho-Mackenzie, Marisol Carreño-Jaimes
An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal level. This disease presents high rate of mortality and complications causing a decrease in the quality of life and increasing the cost of treatment. To estimate the mortality risk of patients undergoing surgery is complex due to the variables associated. The use of clinical decision support systems based on machine learning could help medical staff to improve the results of surgery and get a better understanding of the disease...
August 2016: Journal of Biomedical Informatics
Olivier Sobrie, Mohammed El Amine Lazouni, Saïd Mahmoudi, Vincent Mousseau, Marc Pirlot
BACKGROUND AND OBJECTIVE: The principal challenges in the field of anesthesia and intensive care consist of reducing both anesthetic risks and mortality rate. The ASA score plays an important role in patients' preanesthetic evaluation. In this paper, we propose a methodology to derive simple rules which classify patients in a category of the ASA scale on the basis of their medical characteristics. METHODS: This diagnosis system is based on MR-Sort, a multiple criteria decision analysis model...
September 2016: Computer Methods and Programs in Biomedicine
Manfredo Atzori, Arjan Gijsberts, Claudio Castellini, Barbara Caputo, Anne-Gabrielle Mittaz Hager, Simone Elsig, Giorgio Giatsidis, Franco Bassetto, Henning Müller
Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation...
2016: Journal of Rehabilitation Research and Development
Kevin Bretonnel Cohen, Benjamin Glass, Hansel M Greiner, Katherine Holland-Bouley, Shannon Standridge, Ravindra Arya, Robert Faist, Diego Morita, Francesco Mangano, Brian Connolly, Tracy Glauser, John Pestian
We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do...
2016: Biomedical Informatics Insights
Hsin-Yun Wu, Cihun-Siyong Alex Gong, Shih-Pin Lin, Kuang-Yi Chang, Mei-Yung Tsou, Chien-Kun Ting
Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model...
2016: Scientific Reports
Paul Thottakkara, Tezcan Ozrazgat-Baslanti, Bradley B Hupf, Parisa Rashidi, Panos Pardalos, Petar Momcilovic, Azra Bihorac
OBJECTIVE: To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. DESIGN: Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. PATIENTS: 50,318 adult patients undergoing major surgery. MEASUREMENTS: We evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury...
2016: PloS One
Hideo Chihara, Naoya Oishi, Akira Ishii, Toshihiro Munemitsu, Daisuke Arai, Hiroyuki Ikeda, Susumu Miyamoto
BACKGROUND AND AIMS: Detecting detailed atherosclerotic plaques is important to reduce risk factors during surgery. However, there are few methods to evaluate them during surgery. The aim of this study was to establish an in vivo, non-contact, and label-free imaging method for identifying atherosclerotic plaque lesions from outside vessels with a diffuse-reflectance near-infrared (NIR) hyperspectral imaging (HSI) system. METHODS: NIR spectra between 1000 and 2350 nm were measured using an NIR HSI imaging system outside the exposed abdominal aorta in five Watanabe Heritable Hyperlipidemic (WHHL) rabbits in vivo...
July 2016: Atherosclerosis
Woojae Kim, Ku Sang Kim, Rae Woong Park
OBJECTIVES: Breast cancer has a high rate of recurrence, resulting in the need for aggressive treatment and close follow-up. However, previously established classification guidelines, based on expert panels or regression models, are controversial. Prediction models based on machine learning show excellent performance, but they are not widely used because they cannot explain their decisions and cannot be presented on paper in the way that knowledge is customarily represented in the clinical world...
April 2016: Healthcare Informatics Research
Patrick C Sanger, Gabrielle H van Ramshorst, Ezgi Mercan, Shuai Huang, Andrea L Hartzler, Cheryl A L Armstrong, Ross J Lordon, William B Lober, Heather L Evans
BACKGROUND: Surgical site infection (SSI) remains a common, costly, and morbid health care-associated infection. Early detection can improve outcomes, yet previous risk models consider only baseline risk factors (BF) not incorporating a proximate and timely data source-the wound itself. We hypothesize that incorporation of daily wound assessment improves the accuracy of SSI identification compared with traditional BF alone. STUDY DESIGN: A prospective cohort of 1,000 post open abdominal surgery patients at an academic teaching hospital were examined daily for serial features (SF), for example, wound characteristics and vital signs, in addition to standard BF, for example, wound class...
August 2016: Journal of the American College of Surgeons
David Whiteside, Douglas N Martini, Adam S Lepley, Ronald F Zernicke, Grant C Goulet
BACKGROUND: Ulnar collateral ligament (UCL) reconstruction surgeries in Major League Baseball (MLB) have increased significantly in recent decades. Although several risk factors have been proposed, a scientific consensus is yet to be reached, providing challenges to those tasked with preventing UCL injuries. PURPOSE: To identify significant predictors of UCL reconstruction in MLB pitchers. STUDY DESIGN: Case control study; Level of evidence, 3...
September 2016: American Journal of Sports Medicine
Ali Kamen, Shanhui Sun, Shaohua Wan, Stefan Kluckner, Terrence Chen, Alexander M Gigler, Elfriede Simon, Maximilian Fleischer, Mehreen Javed, Samira Daali, Alhadi Igressa, Patra Charalampaki
Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept...
2016: BioMed Research International
Jonathan Kanevsky, Jason Corban, Richard Gaster, Ari Kanevsky, Samuel Lin, Mirko Gilardino
Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process "big data" using conventional techniques...
May 2016: Plastic and Reconstructive Surgery
Hadi Tadayyon, Lakshmanan Sannachi, Mehrdad Gangeh, Ali Sadeghi-Naini, William Tran, Maureen E Trudeau, Kathleen Pritchard, Sonal Ghandi, Sunil Verma, Gregory J Czarnota
PURPOSE: This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC). METHODS: Using a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest...
April 20, 2016: Oncotarget
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