Isaac Tranter-Entwistle1, Holly Wang2, Kenny Daly2, Scott Maxwell2, Saxon Connor3. 1. Department of General Surgery, Christchurch Hospital, Christchurch, New Zealand. tranter.isaac@gmail.com. 2. Decision Support, Christchurch Hospital, Christchurch, New Zealand. 3. Department of General Surgery, Christchurch Hospital, Christchurch, New Zealand.
Abstract
BACKGROUND: Artificial intelligence is touted as the future of medicine. Classical algorithms for the detection of common bile duct stones (CBD) have had poor clinical uptake due to low accuracy. This study explores the challenges of developing and implementing a machine-learning model for the prediction of CBD stones in patients presenting with acute biliary disease (ABD). METHODS: All patients presenting acutely to Christchurch Hospital over a two-year period with ABD were retrospectively identified. Clinical data points including lab test results, demographics and ethnicity were recorded. Several statistical techniques were utilised to develop a machine-learning model. Issues with data collection, quality, interpretation and barriers to implementation were identified and highlighted. RESULTS: Issues with patient identification, coding accuracy, and implementation were encountered. In total, 1315 patients met inclusion criteria. Incorrect international classification of disease 10 (ICD-10) coding was noted in 36% (137/382) of patients recorded as having CBD stones. Patients with CBD stones were significantly older and had higher aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin and gamma-glutamyl transferase (GGT) levels (p < 0.001). The no information rate was 81% (1070/1315 patients). The optimum model developed was the gradient boosted model with a PPV of 67%, NPV of 87%, sensitivity of 37% and a specificity of 96% for common bile duct stones. CONCLUSION: This paper highlights the utility of machine learning in predicting CBD stones. Accuracy is limited by current data and issues do exist around both the ethics and practicality of implementation. Regardless, machine learning represents a promising new paradigm for surgical practice.
BACKGROUND: Artificial intelligence is touted as the future of medicine. Classical algorithms for the detection of common bile duct stones (CBD) have had poor clinical uptake due to low accuracy. This study explores the challenges of developing and implementing a machine-learning model for the prediction of CBD stones in patients presenting with acute biliary disease (ABD). METHODS: All patients presenting acutely to Christchurch Hospital over a two-year period with ABD were retrospectively identified. Clinical data points including lab test results, demographics and ethnicity were recorded. Several statistical techniques were utilised to develop a machine-learning model. Issues with data collection, quality, interpretation and barriers to implementation were identified and highlighted. RESULTS: Issues with patient identification, coding accuracy, and implementation were encountered. In total, 1315 patients met inclusion criteria. Incorrect international classification of disease 10 (ICD-10) coding was noted in 36% (137/382) of patients recorded as having CBD stones. Patients with CBD stones were significantly older and had higher aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin and gamma-glutamyl transferase (GGT) levels (p < 0.001). The no information rate was 81% (1070/1315 patients). The optimum model developed was the gradient boosted model with a PPV of 67%, NPV of 87%, sensitivity of 37% and a specificity of 96% for common bile duct stones. CONCLUSION: This paper highlights the utility of machine learning in predicting CBD stones. Accuracy is limited by current data and issues do exist around both the ethics and practicality of implementation. Regardless, machine learning represents a promising new paradigm for surgical practice.
Authors: Gobind Anand; Yuval A Patel; Hsin-Chieh Yeh; Mouen A Khashab; Anne Marie Lennon; Eun Ji Shin; Marcia I Canto; Patrick I Okolo; Anthony N Kalloo; Vikesh K Singh Journal: Can J Gastroenterol Hepatol Date: 2016-04-28
Authors: Christopher J Kelly; Alan Karthikesalingam; Mustafa Suleyman; Greg Corrado; Dominic King Journal: BMC Med Date: 2019-10-29 Impact factor: 8.775