Artificial intelligence assistance for women who had spot compression view : reducing recall rates for digital mammography
BACKGROUND: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views.
PURPOSE: To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view.
MATERIAL AND METHODS: From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P < 0.001) significantly improved with AI assistance.
CONCLUSION: AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:64 |
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| Enthalten in: |
Acta radiologica (Stockholm, Sweden : 1987) - 64(2023), 5 vom: 23. Mai, Seite 1808-1815 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, Si Eun [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 05.05.2023 Date Revised 05.05.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1177/02841851221140556 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM349394563 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views | ||
| 520 | |a PURPOSE: To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view | ||
| 520 | |a MATERIAL AND METHODS: From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy | ||
| 520 | |a RESULTS: Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P < 0.001) significantly improved with AI assistance | ||
| 520 | |a CONCLUSION: AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Digital mammography | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a breast neoplasms | |
| 650 | 4 | |a computer-assisted | |
| 650 | 4 | |a diagnosis | |
| 700 | 1 | |a Kim, Ga Ram |e verfasserin |4 aut | |
| 700 | 1 | |a Yoon, Jung Hyun |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Kyunghwa |e verfasserin |4 aut | |
| 700 | 1 | |a Son, Won Jeong |e verfasserin |4 aut | |
| 700 | 1 | |a Shin, Hye Jung |e verfasserin |4 aut | |
| 700 | 1 | |a Moon, Hee Jung |e verfasserin |4 aut | |
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