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Development and Validation of an AI-Based Decision Support Tool (PROMETHEUS) for Predicting Methotrexate Success in Ectopic Pregnancy
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Objective: E
ctopic pregnancy (EP), a life-threatening condition
in early pregnancy, requires accurate and timely management to reduce
morbidity and mortality. While methotrexate therapy is a widely used
non-invasive treatment, predicting its success remains challenging. This
study introduces PROMETHEUS (Prediction of Methotrexate for Ectopic
Pregnancy Treatment Success), an AI-driven decision support tool
designed to assist clinicians in selecting optimal treatment strategies,
aiming to minimize unnecessary surgeries and enhance patient outcomes.
Methods:
A retrospective nested cohort study analyzed 602 cases
of EP diagnosed at Tepecik Education and Research Hospital between 2013
and 2023. Patients were grouped based on treatment received: single-dose
methotrexate, two-dose methotrexate, surgery, or surgery following
failed methotrexate therapy. Twenty-four clinical parameters were
recorded, with 22 included in the final analysis. PROMETHEUS was
developed using 15 AI algorithms, including Bagging, J48, and Random
Forest. The dataset was divided into training (80%) and validation
(20%) cohorts.
Results:
Key findings highlighted significant
clinical differences between treatment groups, such as larger ectopic
mass sizes and altered blood counts in patients requiring surgery after
methotrexate failure. All algorithms used for modelling Prometheus
achieved high accuracy (p<0.001), of them Bagging algorithm
demonstrated an optimal balance between sensitivity (92.7%) and
specificity (94.8%), along with the highest AUC (93.8%)
(p<0.001).
Conclusion:
PROMETHEUS represents an
innovation in ectopic pregnancy management, offering a high-accuracy
AI-based decision support tool to optimize methotrexate therapy
selection. This innovation has the potential to reduce treatment-related
morbidity, streamline clinical workflows, and enhance personalized care.
Future multi-institutional studies are needed to validate PROMETHEUS or
similar AI-based applications and expand their clinical applicability.
Title: Development and Validation of an AI-Based Decision Support Tool (PROMETHEUS) for Predicting Methotrexate Success in Ectopic Pregnancy
Description:
Objective: E
ctopic pregnancy (EP), a life-threatening condition
in early pregnancy, requires accurate and timely management to reduce
morbidity and mortality.
While methotrexate therapy is a widely used
non-invasive treatment, predicting its success remains challenging.
This
study introduces PROMETHEUS (Prediction of Methotrexate for Ectopic
Pregnancy Treatment Success), an AI-driven decision support tool
designed to assist clinicians in selecting optimal treatment strategies,
aiming to minimize unnecessary surgeries and enhance patient outcomes.
Methods:
A retrospective nested cohort study analyzed 602 cases
of EP diagnosed at Tepecik Education and Research Hospital between 2013
and 2023.
Patients were grouped based on treatment received: single-dose
methotrexate, two-dose methotrexate, surgery, or surgery following
failed methotrexate therapy.
Twenty-four clinical parameters were
recorded, with 22 included in the final analysis.
PROMETHEUS was
developed using 15 AI algorithms, including Bagging, J48, and Random
Forest.
The dataset was divided into training (80%) and validation
(20%) cohorts.
Results:
Key findings highlighted significant
clinical differences between treatment groups, such as larger ectopic
mass sizes and altered blood counts in patients requiring surgery after
methotrexate failure.
All algorithms used for modelling Prometheus
achieved high accuracy (p<0.
001), of them Bagging algorithm
demonstrated an optimal balance between sensitivity (92.
7%) and
specificity (94.
8%), along with the highest AUC (93.
8%)
(p<0.
001).
Conclusion:
PROMETHEUS represents an
innovation in ectopic pregnancy management, offering a high-accuracy
AI-based decision support tool to optimize methotrexate therapy
selection.
This innovation has the potential to reduce treatment-related
morbidity, streamline clinical workflows, and enhance personalized care.
Future multi-institutional studies are needed to validate PROMETHEUS or
similar AI-based applications and expand their clinical applicability.
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