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The future of neonatal lung ultrasound: Validation of an artificial intelligence model for interpreting lung scans. A multicentre prospective diagnostic study

医学 胎龄 新生儿学 切断 持续气道正压 前瞻性队列研究 接收机工作特性 肺超声 超声波 呼吸急促 放射科 内科学 怀孕 物理 阻塞性睡眠呼吸暂停 生物 量子力学 遗传学 心动过速
作者
Alessandro Perri,Annamaria Sbordone,Maria Letizia Patti,Stefano Nobile,Chiara Tirone,Lucia Giordano,Milena Tana,Vito D’Andrea,Francesca Priolo,Francesca Serrao,Riccardo Riccardi,Giorgia Prontera,Jacopo Lenkowicz,Luca Boldrini,Giovanni Vento
出处
期刊:Pediatric Pulmonology [Wiley]
卷期号:58 (9): 2610-2618 被引量:1
标识
DOI:10.1002/ppul.26563
摘要

Abstract Background Artificial intelligence (AI) is a promising field in the neonatal field. We focused on lung ultrasound (LU), a useful tool for the neonatologist. Our aim was to train a neural network to create a model able to interpret LU. Methods Our multicentric, prospective study included newborns with gestational age (GA) ≥ 33 + 0 weeks with early tachypnea/dyspnea/oxygen requirements. For each baby, three LU were performed: within 3 h of life (T0), at 4–6 h of life (T1), and in the absence of respiratory support (T2). Each scan was processed to extract the region of interest used to train a neural network to classify it according to the LU score (LUS). We assessed sensitivity, specificity, positive and negative predictive value of the AI model's scores in predicting the need for respiratory assistance with nasal continuous positive airway pressure and for surfactant, compared to an already studied and established LUS. Results We enrolled 62 newborns (GA = 36 ± 2 weeks). In the prediction of the need for CPAP, we found a cutoff of 6 (at T0) and 5 (at T1) for both the neonatal lung ultrasound score (nLUS) and AI score (AUROC 0.88 for T0 AI model, 0.80 for T1 AI model). For the outcome “need for surfactant therapy”, results in terms of area under receiver operator characteristic (AUROC) are 0.84 for T0 AI model and 0.89 for T1 AI model. In the prediction of surfactant therapy, we found a cutoff of 9 for both scores at T0, at T1 the nLUS cutoff was 6, while the AI's one was 5. Classification accuracy was good both at the image and class levels. Conclusions This is, to our knowledge, the first attempt to use an AI model to interpret early neonatal LUS and can be extremely useful for neonatologists in the clinical setting.
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