Retained 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.

In 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.

In 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%.

Software with high sensitivity for diagnosis of retained surgical sponges was developed successfully.