Assessment of artificial intelligence-aided reading in the detection of nasal bone fractures.
Technol Health Care
; 31(3): 1017-1025, 2023.
Article
em En
| MEDLINE
| ID: mdl-36442167
ABSTRACT
BACKGROUND:
Artificial intelligence (AI) technology is a promising diagnostic adjunct in fracture detection. However, few studies describe the improvement of clinicians' diagnostic accuracy for nasal bone fractures with the aid of AI technology.OBJECTIVE:
This study aims to determine the value of the AI model in improving the diagnostic accuracy for nasal bone fractures compared with manual reading.METHODS:
A total of 252 consecutive patients who had undergone facial computed tomography (CT) between January 2020 and January 2021 were enrolled in this study. The presence or absence of a nasal bone fracture was determined by two experienced radiologists. An AI algorithm based on the deep-learning algorithm was engineered, trained and validated to detect fractures on CT images. Twenty readers with various experience were invited to read CT images with or without AI. The accuracy, sensitivity and specificity with the aid of the AI model were calculated by the readers.RESULTS:
The deep-learning AI model had 84.78% sensitivity, 86.67% specificity, 0.857 area under the curve (AUC) and a 0.714 Youden index in identifying nasal bone fractures. For all readers, regardless of experience, AI-aided reading had higher sensitivity ([94.00 ± 3.17]% vs [83.52 ± 10.16]%, P< 0.001), specificity ([89.75 ± 6.15]% vs [77.55 ± 11.38]%, P< 0.001) and AUC (0.92 ± 0.04 vs 0.81 ± 0.10, P< 0.001) compared with reading without AI. With the aid of AI, the sensitivity, specificity and AUC were significantly improved in readers with 1-5 years or 6-10 years of experience (all P< 0.05, Table 4). For readers with 11-15 years of experience, no evidence suggested that AI could improve sensitivity and AUC (P= 0.124 and 0.152, respectively).CONCLUSION:
The AI model might aid less experienced physicians and radiologists in improving their diagnostic performance for the localisation of nasal bone fractures on CT images.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Fraturas Ósseas
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article