Objectives To perform a systematic review on the impact of deep learning (DL)-based triage for reducing diagnostic delays and improving patient outcomes in peer-reviewed and pre-print publications. Materials and methods A search was conducted of primary research studies focused on DL-based worklist optimization for diagnostic imaging triage published on multiple databases from January 2018 until July 2024. Extracted data included study design, dataset characteristics, workflow metrics including report turnaround time and time-to-treatment, and patient outcome differences. Further analysis between clinical settings and integration modality was investigated using nonparametric statistics. Risk of bias was assessed with the risk of bias in non-randomized studies-of interventions (ROBINS-I) checklist. Results A total of 38 studies from 20 publications, involving 138,423 images, were analyzed. Workflow interventions concerned pulmonary embolism (n = 8), stroke (n = 3), intracranial hemorrhage (n = 12), and chest conditions (n = 15). Patients in the post DL-triage group had shorter median report turnaround times: a mean difference of 12.3 min (IQR: −25.7, −7.6) for pulmonary embolism, 20.5 min (IQR: −32.1, −9.3) for stroke, 4.3 min (IQR: −8.6, 1.3) for intracranial hemorrhage and 29.7 min (IQR: −2947.7, −18.3) for chest diseases. Sub-group analysis revealed that reductions varied per clinical environment and relative prevalence rates but were the highest when algorithms actively stratified and reordered the radiological worklist, with reductions of −43.7% in report turnaround time compared to −7.6% from widget-based systems (p < 0.01). Conclusion DL-based triage systems had comparable report turnaround time improvements, especially in outpatient and high-prevalence settings, suggesting that AI-based triage holds promise in alleviating radiology workloads. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Can DL-based triage address lengthening imaging report turnaround times and improve patient outcomes across distinct clinical environments? <Emphasis Type="BoldItalic">Findings</Emphasis> DL-based triage improved report turnaround time across disease groups, with higher reductions reported in high-prevalence or lower acuity settings. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> DL-based workflow prioritization is a reliable tool for reducing diagnostic imaging delay for time-sensitive disease across clinical settings. However, further research and reliable metrics are needed to provide specific recommendations with regards to false-negative examinations and multi-condition prioritization. Graphical Abstract

Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes

期刊论文
发表日期：

2025年5月21日

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Eshan Momin,
Tessa Cook,
Gabrielle Gershon,
Jaret Barr,
Carlo N. De Cecco,
Marly van Assen

摘要

Objectives

To perform a systematic review on the impact of deep learning (DL)-based triage for reducing diagnostic delays and improving patient outcomes in peer-reviewed and pre-print publications.

Materials and methods

A search was conducted of primary research studies focused on DL-based worklist optimization for diagnostic imaging triage published on multiple databases from January 2018 until July 2024. Extracted data included study design, dataset characteristics, workflow metrics including report turnaround time and time-to-treatment, and patient outcome differences. Further analysis between clinical settings and integration modality was investigated using nonparametric statistics. Risk of bias was assessed with the risk of bias in non-randomized studies-of interventions (ROBINS-I) checklist.

Results

A total of 38 studies from 20 publications, involving 138,423 images, were analyzed. Workflow interventions concerned pulmonary embolism (n = 8), stroke (n = 3), intracranial hemorrhage (n = 12), and chest conditions (n = 15). Patients in the post DL-triage group had shorter median report turnaround times: a mean difference of 12.3 min (IQR: −25.7, −7.6) for pulmonary embolism, 20.5 min (IQR: −32.1, −9.3) for stroke, 4.3 min (IQR: −8.6, 1.3) for intracranial hemorrhage and 29.7 min (IQR: −2947.7, −18.3) for chest diseases. Sub-group analysis revealed that reductions varied per clinical environment and relative prevalence rates but were the highest when algorithms actively stratified and reordered the radiological worklist, with reductions of −43.7% in report turnaround time compared to −7.6% from widget-based systems (p < 0.01).

Conclusion

DL-based triage systems had comparable report turnaround time improvements, especially in outpatient and high-prevalence settings, suggesting that AI-based triage holds promise in alleviating radiology workloads.

Key Points

Question Can DL-based triage address lengthening imaging report turnaround times and improve patient outcomes across distinct clinical environments?

Findings DL-based triage improved report turnaround time across disease groups, with higher reductions reported in high-prevalence or lower acuity settings.

Clinical relevance DL-based workflow prioritization is a reliable tool for reducing diagnostic imaging delay for time-sensitive disease across clinical settings. However, further research and reliable metrics are needed to provide specific recommendations with regards to false-negative examinations and multi-condition prioritization.

Graphical Abstract