Performance of two different artificial intelligence (AI) methods for assessing carpal bone age compared to the standard Greulich and Pyle method.

Autor: Alaimo D; Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy., Terranova MC; UOC Radiologia Pediatrica Dipartimento di Diagnostica per Immagini e Interventistica, ARNAS, Ospedali Civico, Di Cristina Benfratelli, Palermo, Italy., Palizzolo E; Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy., De Angelis M; Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy., Avella V; Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy., Paviglianiti G; UOC Radiologia Pediatrica Dipartimento di Diagnostica per Immagini e Interventistica, ARNAS, Ospedali Civico, Di Cristina Benfratelli, Palermo, Italy., Lo Re G; Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy., Matranga D; Dipartimento Promozione della Salute, Materno-Infantile (PROMISE), Università Di Palermo, Palermo, Italy., Salerno S; Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy. ssalerno@sirm.org.
Jazyk: angličtina
Zdroj: La Radiologia medica [Radiol Med] 2024 Oct; Vol. 129 (10), pp. 1507-1512. Date of Electronic Publication: 2024 Aug 20.
DOI: 10.1007/s11547-024-01871-2
Abstrakt: Purpose: Evaluate the agreement between bone age assessments conducted by two distinct machine learning system and standard Greulich and Pyle method.
Materials and Methods: Carpal radiographs of 225 patients (mean age 8 years and 10 months, SD = 3 years and 1 month) were retrospectively analysed at two separate institutions (October 2018 and May 2022) by both expert radiologists and radiologists in training as well as by two distinct AI software programmes, 16-bit AI tm and BoneXpert® in a blinded manner.
Results: The bone age range estimated by the 16-bit AI tm system in our sample varied between 1 year and 1 month and 15 years and 8 months (mean bone age 9 years and 5 months SD = 3 years and 3 months). BoneXpert® estimated bone age ranged between 8 months and 15 years and 7 months (mean bone age 8 years and 11 months SD = 3 years and 3 months). The average bone age estimated by the Greulich and Pyle method was between 11 months and 14 years, 9 months (mean bone age 8 years and 4 months SD = 3 years and 3 months). Radiologists' assessments using the Greulich and Pyle method were significantly correlated (Pearson's r > 0.80, p < 0.001). There was no statistical difference between BoneXpert® and 16-bit AI tm (mean difference = - 0.19, 95%CI = (- 0.45; 0.08)), and the agreement between two measurements varies between - 3.45 (95%CI = (- 3.95; - 3.03) and 3.07 (95%CI - 3.03; 3.57).
Conclusions: Both AI methods and GP provide correlated results, although the measurements made by AI were closer to each other compared to the GP method.
(© 2024. The Author(s).)
Databáze: MEDLINE
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