Georgiou Michalis F, Sfakianaki Efrosyni, Diaz-Kanelidis Monica N, Moshiree Baha
Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Department of Radiology, Jackson Memorial Hospital, Miami, FL 33136, USA.
Diagnostics (Basel). 2024 Jun 13;14(12):1240. doi: 10.3390/diagnostics14121240.
The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis.
An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study. The model was trained and tested on a cohort of 1002 patients who underwent GES using a 70/30 stratified split ratio for training vs. testing. The ML software automated the generation of optimal predictive models by employing a combination of data preprocessing, appropriate feature selection, and predictive modeling analysis algorithms.
The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the predictive modeling performance. Several models were developed using different combinations of imaging time points as input features and methodologies to achieve optimal output. By using GE values at time points 0.5 h, 1 h, 1.5 h, 2 h, and 2.5 h as input predictors of the 4 h outcome, the analysis produced an AUC of 90.7% and a balanced accuracy (BA) of 80.0% on the test set. This performance was comparable to the training set results (AUC = 91.5%, BA = 84.7%) within the 95% confidence interval (CI), demonstrating a robust predictive capability. Through feature selection, it was discovered that the 2.5 h GE value alone was statistically significant enough to predict the 4 h outcome independently, with a slightly increased test set performance (AUC = 92.4%, BA = 83.3%), thus emphasizing its dominance as the primary predictor for delayed GE. ROC analysis was also performed for single time imaging points at 1 h and 2 h to assess their independent predictiveness of the 4 h outcome. Furthermore, the ML model was tested for its ability to predict "flipping" cases with normal GE at 1 h and 2 h that became abnormal with delayed GE at 4 h.
An AI/ML model was designed and trained for predicting delayed GE using a limited number of imaging time points in a 4 h GES clinical protocol. This study demonstrates the feasibility of employing ML for GES optimization in the detection of delayed GE and potentially shortening the protocol's time length without compromising diagnostic power.
本研究旨在探讨机器学习（ML）系统优化胃排空闪烁扫描（GES）方案以检测胃排空延迟（GE）的可行性，胃排空延迟被认为是胃轻瘫诊断的主要指征。
使用JADBio自动机器学习（AI）平台开发了一个ML模型。该模型利用摄入标准化放射性标记餐食后不同成像时间点的GE百分比来预测4小时GES研究结束时的正常与延迟GE情况。该模型在1002例接受GES检查的患者队列中进行训练和测试，训练与测试的分层分割比例为70/30。ML软件通过结合数据预处理、适当的特征选择和预测建模分析算法，自动生成最佳预测模型。
采用受试者操作特征（ROC）曲线下面积（AUC）来评估预测建模性能。使用不同成像时间点的组合作为输入特征和方法开发了多个模型，以实现最佳输出。通过将0.5小时、1小时、1.5小时、2小时和2.5小时时间点的GE值作为4小时结果的输入预测因子，分析在测试集上产生的AUC为90.7%，平衡准确率（BA）为80.0%。在95%置信区间（CI）内，该性能与训练集结果（AUC = 91.5%，BA = 84.7%）相当，表明具有强大的预测能力。通过特征选择发现，仅2.5小时的GE值在统计学上就足以独立预测4小时的结果，测试集性能略有提高（AUC = 92.4%，BA = 83.3%），从而强调了其作为延迟GE主要预测因子的主导地位。还对1小时和2小时的单时间成像点进行了ROC分析，以评估它们对4小时结果的独立预测能力。此外，测试了ML模型预测1小时和2小时GE正常但4小时GE延迟的“翻转”病例的能力。
设计并训练了一种AI/ML模型，用于在4小时GES临床方案中使用有限数量的成像时间点预测延迟GE。本研究证明了在检测延迟GE时采用ML优化GES的可行性，并有可能在不影响诊断能力的情况下缩短方案的时间长度。
