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https://www.readbyqxmd.com/read/29926095/automated-performance-metrics-and-machine-learning-algorithms-to-measure-surgeon-performance-and-anticipate-clinical-outcomes-in-robotic-surgery
#1
Andrew J Hung, Jian Chen, Inderbir S Gill
No abstract text is available yet for this article.
June 20, 2018: JAMA Surgery
https://www.readbyqxmd.com/read/29908785/radio-pathomic-maps-of-epithelium-and-lumen-density-predict-the-location-of-high-grade-prostate-cancer
#2
Sean D McGarry, Sarah L Hurrell, Kenneth A Iczkowski, William Hall, Amy L Kaczmarowski, Anjishnu Banerjee, Tucker Keuter, Kenneth Jacobsohn, John D Bukowy, Marja T Nevalainen, Mark D Hohenwalter, William A See, Peter S LaViolette
PURPOSE: This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization. METHODS AND MATERIALS: Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium...
April 24, 2018: International Journal of Radiation Oncology, Biology, Physics
https://www.readbyqxmd.com/read/29881759/predicting-future-elective-colon-resection-for-diverticulitis-using-patterns-of-health-care-utilization
#3
Lucas W Thornblade, David R Flum, Abraham D Flaxman
Background: Recurrent diverticulitis is the most common reason for elective colon surgery and, although professional societies now recommend against early resection, its use continues to rise. Shared decision making decreases use of low-value surgery but identifying which patients are most likely to elect surgery has proven difficult. We hypothesized that Machine Learning algorithms using health care utilization (HCU) data can predict future clinical events including early resection for diverticulitis...
January 24, 2018: EGEMS
https://www.readbyqxmd.com/read/29876695/machine-learning-derived-classifier-predicts-absence-of-persistent-pain-after-breast-cancer-surgery-with-high-accuracy
#4
Jörn Lötsch, Reetta Sipilä, Tiina Tasmuth, Dario Kringel, Ann-Mari Estlander, Tuomo Meretoja, Eija Kalso, Alfred Ultsch
BACKGROUND: Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain. METHODS: Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28-75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36...
June 6, 2018: Breast Cancer Research and Treatment
https://www.readbyqxmd.com/read/29876245/the-impact-of-epilepsy-surgery-on-the-structural-connectome-and-its-relation-to-outcome
#5
Peter N Taylor, Nishant Sinha, Yujiang Wang, Sjoerd B Vos, Jane de Tisi, Anna Miserocchi, Andrew W McEvoy, Gavin P Winston, John S Duncan
Background: Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. Methods: We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network...
2018: NeuroImage: Clinical
https://www.readbyqxmd.com/read/29844970/sample-entropy-analysis-for-the-estimating-depth-of-anaesthesia-through-human-eeg-signal-at-different-levels-of-unconsciousness-during-surgeries
#6
Quan Liu, Li Ma, Shou-Zen Fan, Maysam F Abbod, Jiann-Shing Shieh
Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target...
2018: PeerJ
https://www.readbyqxmd.com/read/29800386/microelectrode-recordings-validate-the-clinical-visualization-of-subthalamic-nucleus-based-on-7t-magnetic-resonance-imaging-and-machine-learning-for-deep-brain-stimulation-surgery
#7
Reuben R Shamir, Yuval Duchin, Jinyoung Kim, Remi Patriat, Odeya Marmor, Hagai Bergman, Jerrold L Vitek, Guillermo Sapiro, Atira Bick, Ruth Eliahou, Renana Eitan, Zvi Israel, Noam Harel
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML)...
May 24, 2018: Neurosurgery
https://www.readbyqxmd.com/read/29799911/performance-of-a-genomic-sequencing-classifier-for-the-preoperative-diagnosis-of-cytologically-indeterminate-thyroid-nodules
#8
Kepal N Patel, Trevor E Angell, Joshua Babiarz, Neil M Barth, Thomas Blevins, Quan-Yang Duh, Ronald A Ghossein, R Mack Harrell, Jing Huang, Giulia C Kennedy, Su Yeon Kim, Richard T Kloos, Virginia A LiVolsi, Gregory W Randolph, Peter M Sadow, Michael H Shanik, Julie A Sosa, S Thomas Traweek, P Sean Walsh, Duncan Whitney, Michael W Yeh, Paul W Ladenson
Importance: Use of next-generation sequencing of RNA and machine learning algorithms can classify the risk of malignancy in cytologically indeterminate thyroid nodules to limit unnecessary diagnostic surgery. Objective: To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules. Design, Setting, and Participants: A blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine-needle aspiration biopsy between June 2009 and December 2010 from 49 academic and community centers in the United States...
May 23, 2018: JAMA Surgery
https://www.readbyqxmd.com/read/29789232/high-grade-serous-ovarian-cancer-use-of-machine-learning-to-predict-abdominopelvic-recurrence-on-ct-on-the-basis-of-serial-cancer-antigen-125-levels
#9
Atul B Shinagare, Patricia Balthazar, Ivan K Ip, Ronilda Lacson, Joyce Liu, Nikhil Ramaiya, Ramin Khorasani
PURPOSE: The aim of this study was to use machine learning to predict abdominal recurrence on CT on the basis of serial cancer antigen 125 (CA125) levels in patients with advanced high-grade serous ovarian cancer on surveillance. METHODS: This institutional review board-approved, HIPAA-compliant, retrospective, hypothesis-generating study included all 57 patients (mean age, 61 ± 11.2 years) with advanced high-grade serous ovarian cancer who underwent cytoreductive surgery from January to December 2012, followed by surveillance abdominopelvic CT and corresponding CA125 levels...
May 19, 2018: Journal of the American College of Radiology: JACR
https://www.readbyqxmd.com/read/29782369/neuroimaging-in-epilepsy
#10
Meneka Kaur Sidhu, John S Duncan, Josemir W Sander
PURPOSE OF REVIEW: Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. RECENT FINDINGS: The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques...
May 17, 2018: Current Opinion in Neurology
https://www.readbyqxmd.com/read/29753683/evaluating-resective-surgery-targets-in-epilepsy-patients-a-comparison-of-quantitative-eeg-methods
#11
Michael Müller, Kaspar Schindler, Marc Goodfellow, Claudio Pollo, Christian Rummel, Andreas Steimer
BACKGROUND: Quantitative analysis of intracranial EEG is a promising tool to assist clinicians in the planning of resective brain surgery in patients suffering from pharmacoresistant epilepsies. Quantifying the accuracy of such tools, however, is nontrivial as a ground truth to verify predictions about hypothetical resections is missing. NEW METHOD: As one possibility to address this, we use customized hypotheses tests to examine the agreement of the methods on a common set of patients...
May 10, 2018: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/29723266/recognition-of-early-stage-thigmotaxis-in-morris-water-maze-test-with-convolutional-neural-network
#12
Akinori Higaki, Masaki Mogi, Jun Iwanami, Li-Juan Min, Hui-Yu Bai, Bao-Shuai Shan, Harumi Kan-No, Shuntaro Ikeda, Jitsuo Higaki, Masatsugu Horiuchi
The Morris water maze test (MWM) is a useful tool to evaluate rodents' spatial learning and memory, but the outcome is susceptible to various experimental conditions. Thigmotaxis is a commonly observed behavioral pattern which is thought to be related to anxiety or fear. This behavior is associated with prolonged escape latency, but the impact of its frequency in the early stage on the final outcome is not clearly understood. We analyzed swim path trajectories in male C57BL/6 mice with or without bilateral common carotid artery stenosis (BCAS) treatment...
2018: PloS One
https://www.readbyqxmd.com/read/29679032/comparison-of-an-unsupervised-machine-learning-algorithm-and-surgeon-diagnosis-in-the-clinical-differentiation-of-metopic-craniosynostosis-and-benign-metopic-ridge
#13
Min-Jeong Cho, Rami R Hallac, Maleeh Effendi, James R Seaward, Alex A Kane
Metopic suture closure can manifest as a benign metopic ridge (BMR), a variant of normal, to "true" metopic craniosynostosis (MCS), which is associated with severe trigonocephaly. Currently, there is no gold standard for how much associated orbitofrontal dysmorphology should trigger surgical intervention. In our study, we used three-dimensional (3D) curvature analysis to separate the phenotypes along the spectrum, and to compare surgeons' thresholds for operation. Three-dimensional curvature analyses on 43 subject patients revealed that the mean curvature of mid-forehead vertical ridge was higher for patients who underwent operation than those who did not undergo operation by 1...
April 20, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29675321/burn-injured-tissue-detection-for-debridement-surgery-through-the-combination-of-non-invasive-optical-imaging-techniques
#14
Juan Heredia-Juesas, Jeffrey E Thatcher, Yang Lu, John J Squiers, Darlene King, Wensheng Fan, J Michael DiMaio, Jose A Martinez-Lorenzo
The process of burn debridement is a challenging technique requiring significant skills to identify the regions that need excision and their appropriate excision depths. In order to assist surgeons, a machine learning tool is being developed to provide a quantitative assessment of burn-injured tissue. This paper presents three non-invasive optical imaging techniques capable of distinguishing four kinds of tissue-healthy skin, viable wound bed, shallow burn, and deep burn-during serial burn debridement in a porcine model...
April 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29669121/machine-learning-as-a-potential-solution-for-shift-during-stereotactic-brain-surgery
#15
Karl R Abi-Aad, Barrett J Anderies, Matthew E Welz, Bernard R Bendok
No abstract text is available yet for this article.
May 1, 2018: Neurosurgery
https://www.readbyqxmd.com/read/29643160/using-machine-learning-techniques-to-develop-forecasting-algorithms-for-postoperative-complications-protocol-for-a-retrospective-study
#16
Bradley A Fritz, Yixin Chen, Teresa M Murray-Torres, Stephen Gregory, Arbi Ben Abdallah, Alex Kronzer, Sherry Lynn McKinnon, Thaddeus Budelier, Daniel L Helsten, Troy S Wildes, Anshuman Sharma, Michael Simon Avidan
INTRODUCTION: Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forecast an individual patient's trajectory in real time remains poor. We propose to apply machine learning techniques to perioperative time-series data to develop algorithms for predicting adverse perioperative outcomes. METHODS AND ANALYSIS: This study will include all adult patients who had surgery at our tertiary care hospital over a 4-year period...
April 10, 2018: BMJ Open
https://www.readbyqxmd.com/read/29554126/spatio-spectral-classification-of-hyperspectral-images-for-brain-cancer-detection-during-surgical-operations
#17
Himar Fabelo, Samuel Ortega, Daniele Ravi, B Ravi Kiran, Coralia Sosa, Diederik Bulters, Gustavo M Callicó, Harry Bulstrode, Adam Szolna, Juan F Piñeiro, Silvester Kabwama, Daniel Madroñal, Raquel Lazcano, Aruma J-O'Shanahan, Sara Bisshopp, María Hernández, Abelardo Báez, Guang-Zhong Yang, Bogdan Stanciulescu, Rubén Salvador, Eduardo Juárez, Roberto Sarmiento
Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis...
2018: PloS One
https://www.readbyqxmd.com/read/29525490/mobile-technology-and-telemedicine-for-shoulder-range-of-motion-validation-of-a-motion-based-machine-learning-software-development-kit
#18
Prem N Ramkumar, Heather S Haeberle, Sergio M Navarro, Assem A Sultan, Michael A Mont, Eric T Ricchetti, Mark S Schickendantz, Joseph P Iannotti
BACKGROUND: Mobile technology offers the prospect of delivering high-value care with increased patient access and reduced costs. Advances in mobile health (mHealth) and telemedicine have been inhibited by the lack of interconnectivity between devices and software and inability to process consumer sensor data. The objective of this study was to preliminarily validate a motion-based machine learning software development kit (SDK) for the shoulder compared with a goniometer for 4 arcs of motion: (1) abduction, (2) forward flexion, (3) internal rotation, and (4) external rotation...
March 7, 2018: Journal of Shoulder and Elbow Surgery
https://www.readbyqxmd.com/read/29513147/postoperative-seizure-outcome-guided-machine-learning-for-interictal-electrocorticography-in-neocortical-epilepsy
#19
Seong-Cheol Park, Chun Kee Chung
BACKGROUND AND PURPOSE: The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathologic activities in neocortical epilepsy. METHODS: Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome...
March 7, 2018: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29498975/the-state-of-technology-in-craniosynostosis
#20
Tyler Safran, Alex Viezel-Mathieu, Benjamin Beland, Alain J Azzi, Rafael Galli, Mirko Gilardino
INTRODUCTION: Craniosynostosis, the premature fusion of ≥1 cranial sutures, is the leading cause of pediatric skull deformities, affecting 1 of every 2000 to 2500 live births worldwide. Technologies used for the management of craniofacial conditions, specifically in craniosynostosis, have been advancing dramatically. This article highlights the most recent technological advances in craniosynostosis surgery through a systematic review of the literature. METHODS: A systematic electronic search was performed using the PubMed database...
March 1, 2018: Journal of Craniofacial Surgery
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