Author:
Chen Yueh-Sheng,Luo Sheng-Dean,Lee Chi-Hsun,Lin Jian-Feng,Lin Te-Yen,Ko Sheung-Fat,Yu Chiun-Chieh,Chiang Pi-Ling,Wang Cheng-Kang,Chiu I.-Min,Huang Yii-Ting,Tai Yi-Fan,Chiang Po-Teng,Lin Wei-Che
Abstract
Abstract
Objective
We aimed to develop a deep learning artificial intelligence (AI) algorithm to detect impacted animal bones on lateral neck radiographs and to assess its effectiveness for improving the interpretation of lateral neck radiographs.
Methods
Lateral neck radiographs were retrospectively collected for patients with animal bone impaction between January 2010 and March 2020. Radiographs were then separated into training, validation, and testing sets. A total of 1733 lateral neck radiographs were used to develop the deep learning algorithm. The testing set was assessed for the stand-alone deep learning AI algorithm and for human readers (radiologists, radiology residents, emergency physicians, ENT physicians) with and without the aid of the AI algorithm. Another radiograph cohort, collected from April 1, 2020, to June 30, 2020, was analyzed to simulate clinical application by comparing the deep learning AI algorithm with radiologists’ reports.
Results
In the testing set, the sensitivity, specificity, and accuracy of the AI model were 96%, 90%, and 93% respectively. Among the human readers, all physicians of different subspecialties achieved a higher accuracy with AI-assisted reading than without. In the simulation set, among the 20 cases positive for animal bones, the AI model accurately identified 3 more cases than the radiologists’ reports.
Conclusion
Our deep learning AI model demonstrated a higher sensitivity for detection of animal bone impaction on lateral neck radiographs without an increased false positive rate. The application of this model in a clinical setting may effectively reduce time to diagnosis, accelerate workflow, and decrease the use of CT.
Funder
Kaohsiung Chang Gung Memorial Hospital
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
Reference28 articles.
1. Sands NB, Richardson K, Mlynarek A (2012) A bone to pick? Fish bones of the upper aerodigestive tract: review of the literature. J Otolaryngol Head Neck Surg 41:374–380
2. Connolly AA, Birchall M, Walsh-Waring GP, Moore-Gillon V (1992) Ingested foreign bodies: patient-guided localization is a useful clinical tool. Clin Otolaryngol Allied Sci 17:520–524
3. Castan Senar A, Dinu LE, Artigas JM, Larrosa R, Navarro Y, Angulo E (2017) Foreign bodies on lateral neck radiographs in adults: imaging findings and common pitfalls. Radiographics 37:323–345
4. Haglund S, Haverling M, Kuylenstierna R, Lind MG (1978) Radiographic diagnosis of foreign bodies in the oesophagus. J Laryngol Otol 92:1117–1125
5. Malik SA, Qureshi IA, Muhammad R (2018) diagnostic accuracy of plain X-ray lateral neck in the diagnosis of cervical esophageal foreign bodies keeping oesophagoscopy as gold standard. J Ayub Med Coll Abbottabad 30:386–388