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Chinese Journal of Medical Ultrasound (Electronic Edition) ›› 2021, Vol. 18 ›› Issue (02): 177-181. doi: 10.3877/cma.j.issn.1672-6448.2021.02.010

Special Issue:

• Superficial Parts Ultrasound • Previous Articles     Next Articles

Value of artificial intelligent S-Detect technique combined with calcification characteristics in differential diagnosis of thyroid nodules

Mingdi Fang1, Mei Peng1,(), Yu Bi1   

  1. 1. Department of Ultrasound, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601 , China
  • Received:2020-03-02 Online:2021-02-01 Published:2021-02-01
  • Contact: Mei Peng

Abstract:

Objective

To assess the value of S-Detect combined with calcification characteristics in the diagnosis of benign and malignant thyroid nodules.

Methods

Ninety-four patients with 94 thyroid nodules were examined by conventional ultrasonography and S-Detect technique at the Second Affiliated Hospital of Anhui Medical University from July 2019 to February 2020, and the diagnostic efficacy of conventional ultrasound, S-Detect,and S-Detect combined with calcification characteristics were analyzed according to the postoperative pathological results.

Results

As confirmed by surgical pathology, of 94 thyroid nodules in 94 patients, 37 were benign and 57 were malignant. The sensitivity, specificity, and accuracy of conventional ultrasound, S-Detect, and S-Detect combined with calcification characteristics in the differential diagnosis of benign and malignant thyroid nodules were 91.2%, 91.8%, and 91.4%, 96.4%, 81.1%, and 90.4%, and 98.2%, 81.8%, and 92.5%, respectively. The areas under the ROC curves of conventional ultrasound, S-Detect, and S-Detect combined with calcification characteristics were 0.879, 0.864, and 0.890, respectively, with the value of S-Detect combined with calcification characteristics being significantly higher than those of conventional ultrasound and S-Detect (Z=2.020, P=0.043; Z=2.231, P= 0.026).

Conclusions

The combination of S-Detect and calcification characteristics can improve the diagnostic efficiency for thyroid nodules.

Key words: Artificial intelligence, Diagnosis, computer-assisted, Ultrasonography, Calcification, Thyroid nodule

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