Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?—Systematic Review

Author:

Malík Marek1ORCID,Dzian Anton1,Števík Martin2,Vetešková Štefánia2,Al Hakim Abdulla1,Hliboký Maroš3ORCID,Magyar Ján3,Kolárik Michal3ORCID,Bundzel Marek3,Babič František3ORCID

Affiliation:

1. Department of Thoracic Surgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia

2. Radiology Department, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Kollárova 4248/2, 036 59 Martin, Slovakia

3. Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 040 01 Košice, Slovakia

Abstract

Background: Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the evaluation of LUS videos by artificial intelligence could improve the implementation of LUS in thoracic surgery. Methods: A literature review of the replacement of the CXR by LUS after thoracic surgery and the evaluation of LUS videos by artificial intelligence after thoracic surgery was conducted in Medline. Results: Here, eight out of 10 reviewed studies evaluating LUS in CXR reduction showed that LUS can reduce CXR without a negative impact on patient outcome after thoracic surgery. No studies on the evaluation of LUS signs by artificial intelligence after thoracic surgery were found. Conclusion: LUS can reduce CXR after thoracic surgery. We presume that artificial intelligence could help increase the LUS accuracy, objectify the LUS findings, shorten the learning curve, and decrease the number of inconclusive results. To confirm this assumption, clinical trials are necessary. This research is funded by the Slovak Research and Development Agency, grant number APVV 20-0232.

Funder

Slovak Research and Development Agency

Publisher

MDPI AG

Subject

Clinical Biochemistry

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