Limitations of using artificial intelligence services to analyze chest x-ray imaging
BACKGROUND: Chest X-ray examination is one of the first radiology areas that started applying artificial intelligence, and it is still used to the present. However, when interpreting X-ray scans using artificial intelligence, radiologists still experience several routine restrictions that should be...
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| Format: | Article |
| Language: | English |
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Eco-Vector
2024-12-01
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| Series: | Digital Diagnostics |
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| Online Access: | https://jdigitaldiagnostics.com/DD/article/viewFile/626310/pdf |
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| author | Yuriy A. Vasilev Anton V. Vladzymyrskyy Kirill M. Arzamasov Igor M. Shulkin Elena V. Astapenko Lev D. Pestrenin |
| author_facet | Yuriy A. Vasilev Anton V. Vladzymyrskyy Kirill M. Arzamasov Igor M. Shulkin Elena V. Astapenko Lev D. Pestrenin |
| author_sort | Yuriy A. Vasilev |
| collection | DOAJ |
| description | BACKGROUND: Chest X-ray examination is one of the first radiology areas that started applying artificial intelligence, and it is still used to the present. However, when interpreting X-ray scans using artificial intelligence, radiologists still experience several routine restrictions that should be considered in issuing a medical report and require the attention of artificial intelligence developers to further improve the algorithms and increase their efficiency.
AIM: To identify restrictions of artificial intelligence services for analyzing chest X-ray images and assesses the clinical significance of these restrictions.
MATERIALS AND METHODS: A retrospective analysis was performed for 155 cases of discrepancies between the conclusions of artificial intelligence services and medical reports when analyzing chest X-ray images. All cases included in the study were obtained from the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow.
RESULTS: Of the 155 analyzed difference cases, 48 (31.0%) were false-positive and 78 (50.3%) were false-negative cases. The remaining 29 (18.7%) cases were removed from further studies because they were true positive (27) or true negative (2) in the expert review. Most (93.8%) of the 48 false-positive cases were due to the artificial intelligence service mistaking normal chest anatomy (97.8% of cases) or catheter shadow (2.2% of cases) for pneumothorax signs. Overlooked clinically significant pathologies accounted for 22.0% of false-negative scans. Nearly half of these cases (44.4%) were overlooked lung nodules. Lung calcifications (60.9%) were the most common clinically insignificant pathology.
CONCLUSIONS: Artificial intelligence services demonstrate a tendency toward over diagnosis. All false-positive cases were associated with erroneous detection of clinically significant pathology: pneumothorax, lung nodules, and pulmonary consolidation. Among false-negative cases, the rate of overlooked clinically significant pathology was low, which accounted for less than one-fourth. |
| format | Article |
| id | doaj-art-e5729eaa537848a8aa7d2e40b0f9ff9d |
| institution | OA Journals |
| issn | 2712-8490 2712-8962 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Eco-Vector |
| record_format | Article |
| series | Digital Diagnostics |
| spelling | doaj-art-e5729eaa537848a8aa7d2e40b0f9ff9d2025-08-20T02:11:34ZengEco-VectorDigital Diagnostics2712-84902712-89622024-12-015340742010.17816/DD62631076688Limitations of using artificial intelligence services to analyze chest x-ray imagingYuriy A. Vasilev0https://orcid.org/0000-0002-5283-5961Anton V. Vladzymyrskyy1https://orcid.org/0000-0002-2990-7736Kirill M. Arzamasov2https://orcid.org/0000-0001-7786-0349Igor M. Shulkin3https://orcid.org/0000-0002-7613-5273Elena V. Astapenko4https://orcid.org/0009-0006-6284-2088Lev D. Pestrenin5https://orcid.org/0000-0002-1786-4329Research and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesResearch and Practical Clinical Center for Diagnostics and Telemedicine TechnologiesBACKGROUND: Chest X-ray examination is one of the first radiology areas that started applying artificial intelligence, and it is still used to the present. However, when interpreting X-ray scans using artificial intelligence, radiologists still experience several routine restrictions that should be considered in issuing a medical report and require the attention of artificial intelligence developers to further improve the algorithms and increase their efficiency. AIM: To identify restrictions of artificial intelligence services for analyzing chest X-ray images and assesses the clinical significance of these restrictions. MATERIALS AND METHODS: A retrospective analysis was performed for 155 cases of discrepancies between the conclusions of artificial intelligence services and medical reports when analyzing chest X-ray images. All cases included in the study were obtained from the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow. RESULTS: Of the 155 analyzed difference cases, 48 (31.0%) were false-positive and 78 (50.3%) were false-negative cases. The remaining 29 (18.7%) cases were removed from further studies because they were true positive (27) or true negative (2) in the expert review. Most (93.8%) of the 48 false-positive cases were due to the artificial intelligence service mistaking normal chest anatomy (97.8% of cases) or catheter shadow (2.2% of cases) for pneumothorax signs. Overlooked clinically significant pathologies accounted for 22.0% of false-negative scans. Nearly half of these cases (44.4%) were overlooked lung nodules. Lung calcifications (60.9%) were the most common clinically insignificant pathology. CONCLUSIONS: Artificial intelligence services demonstrate a tendency toward over diagnosis. All false-positive cases were associated with erroneous detection of clinically significant pathology: pneumothorax, lung nodules, and pulmonary consolidation. Among false-negative cases, the rate of overlooked clinically significant pathology was low, which accounted for less than one-fourth.https://jdigitaldiagnostics.com/DD/article/viewFile/626310/pdfartificial intelligencechest x-rayreproducibility of resultsreliability |
| spellingShingle | Yuriy A. Vasilev Anton V. Vladzymyrskyy Kirill M. Arzamasov Igor M. Shulkin Elena V. Astapenko Lev D. Pestrenin Limitations of using artificial intelligence services to analyze chest x-ray imaging Digital Diagnostics artificial intelligence chest x-ray reproducibility of results reliability |
| title | Limitations of using artificial intelligence services to analyze chest x-ray imaging |
| title_full | Limitations of using artificial intelligence services to analyze chest x-ray imaging |
| title_fullStr | Limitations of using artificial intelligence services to analyze chest x-ray imaging |
| title_full_unstemmed | Limitations of using artificial intelligence services to analyze chest x-ray imaging |
| title_short | Limitations of using artificial intelligence services to analyze chest x-ray imaging |
| title_sort | limitations of using artificial intelligence services to analyze chest x ray imaging |
| topic | artificial intelligence chest x-ray reproducibility of results reliability |
| url | https://jdigitaldiagnostics.com/DD/article/viewFile/626310/pdf |
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