Scaramozzino Marco Umberto, Levi Guido, Sapone Giovanni, Romeo Plastina Ubaldo
Department of Pulmonology, La Madonnina Clinic, Reggio Calabria, ITA.
Department of Thoracic Endoscopy, Tirrenia Hospital, Reggio Calabria, ITA.
Cureus. 2023 May 25;15(5):e39464. doi: 10.7759/cureus.39464. eCollection 2023 May.
Physicians use auscultation as a standard method of thoracic examination: it is simple, reliable, non-invasive, and widely accepted. Artificial intelligence (AI) is the new frontier of thoracic examination as it makes it possible to integrate all available data (clinical, instrumental, laboratory, functional), allowing for objective assessments, precise diagnoses, and even the phenotypical characterization of lung diseases. Increasing the sensitivity and specificity of examinations helps provide tailored diagnostic and therapeutic indications, which also take into account the patient's clinical history and comorbidities. Several clinical studies, mainly conducted in children, have shown a good concordance between traditional and AI-assisted auscultation in detecting fibrotic diseases. On the other hand, the use of AI for the diagnosis of obstructive pulmonary disease is still debated as it gave inconsistent results when detecting certain types of lung noises, such as wet and dry crackles. Therefore, the application of AI in clinical practice needs further investigation. In particular, the pilot case report aims to address the use of this technology in restrictive lung disease, which in this specific case is pulmonary sarcoidosis. In the case we present, data integration allowed us to make the right diagnosis, avoid invasive procedures, and reduce the costs for the national health system; we show that integrating technologies can improve the diagnosis of restrictive lung disease. Randomized controlled trials will be needed to confirm the conclusions of this preliminary work.
它简单、可靠、无创且被广泛接受。人工智能（AI）是胸部检查的新前沿，因为它能够整合所有可用数据（临床、仪器检查、实验室、功能数据），从而实现客观评估、精确诊断，甚至对肺部疾病进行表型特征分析。提高检查的敏感性和特异性有助于提供个性化的诊断和治疗建议，同时也会考虑患者的临床病史和合并症。多项主要针对儿童进行的临床研究表明，在检测纤维化疾病方面，传统听诊与人工智能辅助听诊之间具有良好的一致性。另一方面，人工智能在阻塞性肺疾病诊断中的应用仍存在争议，因为在检测某些类型的肺部声音（如湿啰音和干啰音）时，其结果并不一致。因此，人工智能在临床实践中的应用需要进一步研究。特别是，本试点病例报告旨在探讨该技术在限制性肺疾病中的应用，在本特定病例中为肺结节病。在我们呈现的病例中，数据整合使我们能够做出正确诊断，避免侵入性操作，并降低国家卫生系统的成本；我们表明，整合技术可以改善限制性肺疾病的诊断。需要进行随机对照试验来证实这项初步工作的结论。
