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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedLuximon DC, Ritter T, Fields E
Development and interinstitutional validation of an automatic vertebral-body misalignment error detector for cone-beam CT-guided radiotherapy.
The purpose of this study was to develop an automatic error detection algorithm that uses a three-branch convolutional neural network error detection model (EDM) to detect off-by-one vertebral-body misalignments using planning computed tomography (CT) images and setup CBCT images. The researchers collected algorithm training and test data from 480 patients undergoing radiotherapy treatment at two radiotherapy clinics. One model (EDM(1) ) was trained solely on data from institution 1. EDM(1) was further trained on a dataset from institution 2 to produce a fine-tuned model, EDM(2) . Another model, EDM(3), was trained using a training dataset composed of data from both institutions. The study found that when applied to the combined test set, EDM(1) , EDM(2) , and EDM(3) resulted in an area under curve of 99.5%, 99.4%, and 99.5%, respectively. EDM(1) achieved a sensitivity of 96% and 88% on Institution 1 and Institution 2 test set, respectively. EDM(2) obtained a sensitivity of 95% on each institution's test set. EDM(3) achieved a sensitivity of 95% and 88% on Institution 1 and Institution 2 test set, respectively. The researchers concluded that in CBCT-guided radiotherapy, the proposed algorithm demonstrated accuracy in identifying off-by-one vertebral-body misalignments that was sufficiently high enough to justify implementation in practice.
AHRQ-funded; HS026486.
Citation: Luximon DC, Ritter T, Fields E .
Development and interinstitutional validation of an automatic vertebral-body misalignment error detector for cone-beam CT-guided radiotherapy.
Med Phys 2022 Oct;49(10):6410-23. doi: 10.1002/mp.15927..
Keywords: Imaging, Diagnostic Safety and Quality, Medical Errors, Patient Safety
Long S, Thomas GW, Karam MD
Surgical skill can be objectively measured from fluoroscopic images using a novel image-based Decision Error Analysis (IDEA) score.
This study introduces and evaluates a novel Image-based Decision Error Analysis (IDEA) score that captures performance during fluoroscopically assisted wire navigation. Findings showed that the fluoroscopic images obtained in the course of placing a guide wire contained a rich amount of information related to surgical skill. The IDEA scoring provided a basis for evaluating the competence of a resident. The score can be used to assess skill at key timepoints throughout residency, such as when rotating onto/off of a new surgical service and before performing certain procedures in the operating room, or as a tool for debriefing/providing feedback after a procedure is completed.
AHRQ-funded; HS022077; HS025353.
Citation: Long S, Thomas GW, Karam MD .
Surgical skill can be objectively measured from fluoroscopic images using a novel image-based Decision Error Analysis (IDEA) score.
Clin Orthop Relat Res 2021 Jun;479(6):1386-94. doi: 10.1097/corr.0000000000001623..
Keywords: Orthopedics, Surgery, Decision Making, Medical Errors, Adverse Events, Imaging