Artificial intelligence’s contribution to early pulmonary lesion detection in chest X-rays: insights from two retrospective studies on a Czech population

Česká verzia

Authors: Martin Černý ^1,2; Daniel Kvak ^1,3; Daniel Schwarz ⁴; Hynek Mírka ⁵; Jakub Dandár ¹
Authors place of work: Carebot s. r. o. ¹; Neurochirurgická a neuroonkologická klinika 1 LF UK a ÚVN Praha ²; Ústav simulační medicíny LF MU v Brně ³; Institut biostatistiky a analýz LF MU v Brně ⁴; Klinika zobrazovacích metod LF UK a FN Plzeň ⁵
Published in the journal: Čas. Lék. čes. 2023; 162: 283-289
Category: Original Article

Summary

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI’s role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions.

We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists.

In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

Keywords:

artificial intelligence, deep learning, computer-aided diagnosis, chest X-ray, lung cancer, solitary pulmonary nodules, pulmonary masses

Zdroje

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Štítky

Addictology Allergology and clinical immunology Angiology Audiology Clinical biochemistry Dermatology & STDs Paediatric gastroenterology Paediatric surgery Paediatric cardiology Paediatric neurology Paediatric ENT Paediatric psychiatry Paediatric rheumatology Diabetology Pharmacy Vascular surgery Pain management Dental Hygienist

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