[pdf] [BibTex]

Note: article

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patient's safety in case of unrecognized findings in chest radiographs (CXR). The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization. To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object. The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.

[pdf]

Note: article

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patient's safety in case of unrecognized findings in chest radiographs (CXR). The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization. To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object. The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.

[pdf] [BibTex]

Note: article

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patient's safety in case of unrecognized findings in chest radiographs (CXR). The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization. To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object. The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.

[pdf]

Note: article

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patient's safety in case of unrecognized findings in chest radiographs (CXR). The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization. To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object. The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.

[pdf]

Note: article

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patient's safety in case of unrecognized findings in chest radiographs (CXR). The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization. To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object. The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.

[pdf] [BibTex]

Note: article

Abstract: Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patient's safety in case of unrecognized findings in chest radiographs (CXR). The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization. To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object. The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.