2018 Robotic Scene Segmentation Challenge

In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modifications on U-Nets and other popular CNN architectures. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs

Predicting intraoperative complications and 30-days morbidity using machine learning techniques for patients undergoing robotic partial nephrectomy (RPN)

Introduction & Objectives: Personalization of a patient risk profile using instruments such as RENAL, PADUA, and MAP scores have limited clinical value. High morbidity of the RPN is attributed to the combination of tumor complexity, patient-related comorbidities, tumor surroundings, and surgeon experience. Predictive models built using Machine Learning (ML) could play a significant role in preempting events, timely intervention and improving patient outcomes. Our objective was to predict Intraoperative Complications (IOC) and 30-day Morbidity (M) as a prelude to the prospective deployment of models in a clinical setting at VCQI collaborating institutions to evolve personalized management strategies. Materials & Methods: Predictive models were developed using Logistic Regression, Random Forest, and Neural Networks. Models to predict IOC were trained using patient demographics and preoperative data. In addition to the above data, intraoperative data was used to build models to predict M. Model performance on the test dataset was assessed using Area Under Receiver Operating Curve (AUC-ROC), and Area Under Precision-Recall Curve (PR-AUC). We used bootstrapping to generate confidence intervals for the scores and performed permutation test to assess if the observed difference in AUC-ROC and PR-AUC was significant. Results: Models for predicting IOC were constructed using data from 1690 patients and 38 variables; the best model had AUC-ROC of 0.825 (95% CI 0.717,0.919), and PR-AUC of 0.585 (95% CI 0.394,0.756). Models for predicting M were trained using data from 1455 patients and 59 variables; the best model had AUC-ROC of 0.868 (95% CI 0.827,0.906), and PR-AUC 0.697 (95% CI 0.603,0.781). Our dataset with pre-defined variables does not account for the temporal shift in patient characteristics, a key limitation of this study. Conclusions: ML model performance during this study is encouraging. These models can be used to predict complications during, and after surgery with good accuracy; paving the way for application in clinical practice to predict, intervene at an opportune time, avert complications and improve patient outcomes. Further validation in a clinical setting would be necessary to establish their clinical value. We propose deploying the models at participating centers contributing to the database (Figure 1)

Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy:a Vattikuti Collective Quality Initiative database study

OBJECTIVE: To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. MATERIALS AND METHODS: The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). RESULTS: The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC-ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR-AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC-ROC of 0.875 (95% CI 0.834, 0.913) and a PR-AUC 0.706 (95% CI, 0.610, 0.790). CONCLUSIONS: The performance of the ML models in the present study was encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes

Predicting intraoperative and postoperative consequential events using machine learning techniques in patients undergoing robotic partial nephrectomy (RPN): Vattikuti Collective Quality Initiative (VCQI) database study.

OBJECTIVE: To predict intraoperative events (IOE) and postoperative events (POE) consequential to the derailment of the ideal clinical course of patient recovery. MATERIAL AND METHODS: Vattikuti Collective Quality Initiative (VCQI), a multi-institutional dataset of patients who underwent Robotic Partial Nephrectomy for kidney tumors. Machine Learning (ML) models were constructed to predict IOE, and POE using Logistic Regression, Random Forest, and Neural Networks. The models to predict IOE used patient demographics and preoperative data. In addition to the above, intraoperative data was used to predict POE. Performance on the test dataset was assessed using Area Under Receiver Operating Curve (AUC-ROC) and Area Under Precision-Recall Curve (PR-AUC). RESULTS: The rate of IOE and POE was 5.62% and 20.98%, respectively. Models for predicting IOE were constructed using data from 1690 patients and 38 variables; the best model had AUC-ROC of 0.858 (95% CI, 0.762, 0.936), and PR-AUC of 0.590 (95% CI, 0.400, 0.759). Models for predicting POE were trained using data from 1406 patients and 59 variables; the best model had AUC-ROC of 0.875 (95% CI, 0.834, 0.913), and PR-AUC 0.706 (95% CI, 0.610, 0.790). CONCLUSIONS: The performance of the ML models in this study is encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes

Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study

OBJECTIVE: To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. MATERIALS AND METHODS: The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC). RESULTS: The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC-ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR-AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC-ROC of 0.875 (95% CI 0.834, 0.913) and a PR-AUC 0.706 (95% CI, 0.610, 0.790). CONCLUSIONS: The performance of the ML models in the present study was encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes