ArticleJournal of the American College of Surgeons2021
Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items.
Article in Journal of the American College of Surgeons, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 21 citations in OpenAlex.
- Radiographic detection of retained neuropatties in the paranasal sinuses: a cadaveric diagnostic accuracy study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Use of Multimodal Artificial Intelligence in Surgical Instrument Recognition.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review.BioData mining · 2024Review
- Artificial intelligence in the detection of non-biological materials.Emergency radiology · 2024Review
- Gossypiboma left behind in a cesarean section ended up with a failed laparotomic excision, which demanded another laparotomy to remove it: a rare case report.Annals of medicine and surgery (2012) · 2023Article
- A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.International journal of computer assisted radiology and surgery · 2023Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRetained surgical items are a serious human error. Surgical sponges account for 70% of retained surgical items. To prevent retained surgical sponges, it is important to establish a system that can identify errors and avoid the occurrence of adverse events. To date, no computer-aided diagnosis software specialized for detecting retained surgical sponges has been reported. We developed a software program that enables easy and effective computer-aided diagnosis of retained surgical sponges with high sensitivity and specificity using the technique of deep learning, a subfield of artificial intelligence. STUDY
designIn this study, we developed the software by training it through deep learning using a dataset and then validating the software. The dataset consisted of a training set and validation set. We created composite x-rays consisting of normal postoperative x-rays and surgical sponge x-rays for a training set (n = 4,554) and a validation set (n = 470). Phantom x-rays (n = 12) were prepared for software validation. X-rays obtained with surgical sponges inserted into cadavers were used for validation purposes (formalin: Thiel's method = 252:117). In addition, postoperative x-rays without retained surgical sponges were used for the validation of software performance to determine false-positive rates. Sensitivity, specificity, and false positives per image were calculated.
resultsIn the phantom x-rays, both the sensitivity and specificity in software image interpretation were 100%. The software achieved 97.7% sensitivity and 83.8% specificity in the composite x-rays. In the normal postoperative x-rays, 86.6% specificity was achieved. In reading the cadaveric x-rays, the software attained both sensitivity and specificity of >90%.
conclusionsSoftware with high sensitivity for diagnosis of retained surgical sponges was developed successfully.
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