Machine Learning and Artificial Intelligence for Surgical Decision Making

2021 ◽  
Vol 22 (6) ◽  
pp. 626-634
Author(s):  
Saskya Byerly ◽  
Lydia R. Maurer ◽  
Alejandro Mantero ◽  
Leon Naar ◽  
Gary An ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making

scholarly journals 3132 Machine Learning for Prediction of Pathologic Pneumatosis Intestinalis Using CT Scans

2019 ◽  
Vol 3 (s1) ◽  
pp. 60-61
Author(s):  
Kadie Clancy ◽  
Esmaeel Dadashzadeh ◽  
Christof Kaltenmeier ◽  
JB Moses ◽  
Shandong Wu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Pneumatosis Intestinalis ◽  
Learning Models ◽  
Surgical Decision ◽  
Prediction Task ◽  
Svm Model ◽  
Surgical Decision Making ◽  
Intraoperative Visualization ◽  
Machine Learning Models

OBJECTIVES/SPECIFIC AIMS: This retrospective study aims to create and train machine learning models using a radiomic-based feature extraction method for two classification tasks: benign vs. pathologic PI and operation of benefit vs. operation not needed. The long-term goal of our study is to build a computerized model that incorporates both radiomic features and critical non-imaging clinical factors to improve current surgical decision-making when managing PI patients. METHODS/STUDY POPULATION: Searched radiology reports from 2010-2012 via the UPMC MARS Database for reports containing the term “pneumatosis” (subsequently accounting for negations and age restrictions). Our inclusion criteria included: patient age 18 or older, clinical data available at time of CT diagnosis, and PI visualized on manual review of imaging. Cases with intra-abdominal free air were excluded. Collected CT imaging data and an additional 149 clinical data elements per patient for a total of 75 PI cases. Data collection of an additional 225 patients is ongoing. We trained models for two clinically-relevant prediction tasks. The first (referred to as prediction task 1) classifies between benign and pathologic PI. Benign PI is defined as either lack of intraoperative visualization of transmural intestinal necrosis or successful non-operative management until discharge. Pathologic PI is defined as either intraoperative visualization of transmural PI or withdrawal of care and subsequent death during hospitalization. The distribution of data samples for prediction task 1 is 47 benign cases and 38 pathologic cases. The second (referred to as prediction task 2) classifies between whether the patient benefitted from an operation or not. “Operation of benefit” is defined as patients with PI, be it transmural or simply mucosal, who benefited from an operation. “Operation not needed” is defined as patients who were safely discharged without an operation or patients who had an operation, but nothing was found. The distribution of data samples for prediction task 2 is 37 operation not needed cases and 38 operation of benefit cases. An experienced surgical resident from UPMC manually segmented 3D PI ROIs from the CT scans (5 mm Axial cut) for each case. The most concerning ~10-15 cm segment of bowel for necrosis with a 1 cm margin was selected. A total of 7 slices per patient were segmented for consistency. For both prediction task 1 and prediction task 2, we independently completed the following procedure for testing and training: 1.) Extracted radiomic features from the 3D PI ROIs that resulted in 99 total features. 2.) Used LASSO feature selection to determine the subset of the original 99 features that are most significant for performance of the prediction task. 3.) Used leave-one-out cross-validation for testing and training to account for the small dataset size in our preliminary analysis. Implemented and trained several machine learning models (AdaBoost, SVM, and Naive Bayes). 4.) Evaluated the trained models in terms of AUC and Accuracy and determined the ideal model structure based on these performance metrics. RESULTS/ANTICIPATED RESULTS: Prediction Task 1: The top-performing model for this task was an SVM model trained using 19 features. This model had an AUC of 0.79 and an accuracy of 75%. Prediction Task 2: The top-performing model for this task was an SVM model trained using 28 features. This model had an AUC of 0.74 and an accuracy of 64%. DISCUSSION/SIGNIFICANCE OF IMPACT: To the best of our knowledge, this is the first study to use radiomic-based machine learning models for the prediction of tissue ischemia, specifically intestinal ischemia in the setting of PI. In this preliminary study, which serves as a proof of concept, the performance of our models has demonstrated the potential of machine learning based only on radiomic imaging features to have discriminative power for surgical decision-making problems. While many non-imaging-related clinical factors play a role in the gestalt of clinical decision making when PI presents, we have presented radiomic-based models that may augment this decision-making process, especially for more difficult cases when clinical features indicating acute abdomen are absent. It should be noted that prediction task 2, whether or not a patient presenting with PI would benefit from an operation, has lower performance than prediction task 1 and is also a more challenging task for physicians in real clinical environments. While our results are promising and demonstrate potential, we are currently working to increase our dataset to 300 patients to further train and assess our models. References DuBose, Joseph J., et al. “Pneumatosis Intestinalis Predictive Evaluation Study (PIPES): a multicenter epidemiologic study of the Eastern Association for the Surgery of Trauma.” Journal of Trauma and Acute Care Surgery 75.1 (2013): 15-23. Knechtle, Stuart J., Andrew M. Davidoff, and Reed P. Rice. “Pneumatosis intestinalis. Surgical management and clinical outcome.” Annals of Surgery 212.2 (1990): 160.

scholarly journals Artificial Intelligence and Surgical Decision-making

JAMA Surgery ◽  
2020 ◽  
Vol 155 (2) ◽  
pp. 148 ◽  
Author(s):  
Tyler J. Loftus ◽  
Patrick J. Tighe ◽  
Amanda C. Filiberto ◽  
Philip A. Efron ◽  
Scott C. Brakenridge ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making

Utilizing artificial intelligence for surgical decision making in orbital fractures

2019 ◽  
Vol 48 ◽  
pp. 33-34
Author(s):  
Andrew Read-Fuller ◽  
Ritesh Bhattacharjee ◽  
Matthew Kesterke ◽  
Ganesh Sankaranarayanan
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Orbital Fractures ◽  
Surgical Decision ◽  
Surgical Decision Making

scholarly journals Using Machine Learning to Identify Change in Surgical Decision Making in Current Use of Damage Control Laparotomy

2019 ◽  
Vol 228 (3) ◽  
pp. 255-264 ◽  
Author(s):  
John A. Harvin ◽  
Charles E. Green ◽  
Claudia Pedroza ◽  
Jon E. Tyson ◽  
Laura J. Moore ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Damage Control ◽  
Damage Control Laparotomy ◽  
Surgical Decision ◽  
Surgical Decision Making

scholarly journals Data-driven surgical decision-making – the prediction of resectability in patients with glioblastoma using machine learning

2021 ◽  
Vol 1 ◽  
pp. 100502
Author(s):  
J.M. Kernbach ◽  
G. Neuloh ◽  
J. Ort ◽  
K. Hakvoort ◽  
L. Dührsen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Data Driven ◽  
Surgical Decision ◽  
Surgical Decision Making

1228: Assessing the Surgical Decision-Making Ability of Novice and Proficient Urologists

2007 ◽  
Vol 177 (4S) ◽  
pp. 405-405
Author(s):  
Suman Chatterjee ◽  
Jonathon Ng ◽  
Edward D. Matsumoto
Keyword(s):  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making

Myocardial infarction – An important factor for surgical decision-making?

2008 ◽  
Vol 56 (S 1) ◽  
Author(s):  
B Osswald ◽  
U Tochtermann ◽  
S Keller ◽  
D Badowski-Zyla ◽  
V Gegouskov ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making
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