Improving mammography interpretation for both novice and experienced readers : a comparative study of two commercial artificial intelligence software
© 2023. The Author(s), under exclusive licence to European Society of Radiology..
OBJECTIVES: To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software.
METHODS: We compared the performance of two AI software (AI-1 and AI-2) in two experienced and two novice readers for 200 mammographic examinations (80 cancer cases). Two reading sessions were conducted within 4 weeks. The readers rated the likelihood of malignancy (range, 1-7) and the percentage probability of malignancy (range, 0-100%), with and without AI assistance. Differences in AUROC, sensitivity, and specificity were analyzed.
RESULTS: Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [p = 0.005]; 0.91 with AI-2 [p < 0.001]) and experienced readers (0.87 to 0.92 with AI-1 [p < 0.001]; 0.90 with AI-2 [p = 0.004]). Sensitivities increased from 81.3 to 88.8% with AI-1 (p = 0.027) and to 91.3% with AI-2 (p = 0.005) in novice readers, and from 81.9 to 90.6% with AI-1 (p = 0.001) and to 87.5% with AI-2 (p = 0.016) in experienced readers. Specificity did not decrease significantly in both novice (p > 0.999, both) and experienced readers (p > 0.999 with AI-1 and 0.282 with AI-2). There was no significant difference in the performance change depending on the type of AI software (p > 0.999).
CONCLUSION: Commercial AI software improved the diagnostic performance of both novice and experienced readers. The type of AI software used did not significantly impact performance changes. Further validation with a larger number of cases and readers is needed.
CLINICAL RELEVANCE STATEMENT: Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers.
KEY POINTS: • Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
European radiology - (2023) vom: 08. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kim, Hee Jeong [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 08.11.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s00330-023-10422-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364301317 |
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| 100 | 1 | |a Kim, Hee Jeong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improving mammography interpretation for both novice and experienced readers |b a comparative study of two commercial artificial intelligence software |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to European Society of Radiology. | ||
| 520 | |a OBJECTIVES: To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software | ||
| 520 | |a METHODS: We compared the performance of two AI software (AI-1 and AI-2) in two experienced and two novice readers for 200 mammographic examinations (80 cancer cases). Two reading sessions were conducted within 4 weeks. The readers rated the likelihood of malignancy (range, 1-7) and the percentage probability of malignancy (range, 0-100%), with and without AI assistance. Differences in AUROC, sensitivity, and specificity were analyzed | ||
| 520 | |a RESULTS: Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [p = 0.005]; 0.91 with AI-2 [p < 0.001]) and experienced readers (0.87 to 0.92 with AI-1 [p < 0.001]; 0.90 with AI-2 [p = 0.004]). Sensitivities increased from 81.3 to 88.8% with AI-1 (p = 0.027) and to 91.3% with AI-2 (p = 0.005) in novice readers, and from 81.9 to 90.6% with AI-1 (p = 0.001) and to 87.5% with AI-2 (p = 0.016) in experienced readers. Specificity did not decrease significantly in both novice (p > 0.999, both) and experienced readers (p > 0.999 with AI-1 and 0.282 with AI-2). There was no significant difference in the performance change depending on the type of AI software (p > 0.999) | ||
| 520 | |a CONCLUSION: Commercial AI software improved the diagnostic performance of both novice and experienced readers. The type of AI software used did not significantly impact performance changes. Further validation with a larger number of cases and readers is needed | ||
| 520 | |a CLINICAL RELEVANCE STATEMENT: Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers | ||
| 520 | |a KEY POINTS: • Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Breast neoplasms | |
| 650 | 4 | |a Computer-assisted diagnosis | |
| 650 | 4 | |a Mammography | |
| 700 | 1 | |a Choi, Woo Jung |e verfasserin |4 aut | |
| 700 | 1 | |a Gwon, Hye Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Jang, Seo Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Chae, Eun Young |e verfasserin |4 aut | |
| 700 | 1 | |a Shin, Hee Jung |e verfasserin |4 aut | |
| 700 | 1 | |a Cha, Joo Hee |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Hak Hee |e verfasserin |4 aut | |
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