Artificial intelligence as an initial reader for double reading in breast cancer screening: a prospective initial study of 32,822 mammograms of the Egyptian population.

Autor:	Mansour, Sahar, Sweed, Enas, Gomaa, Mohammed Mohammed Mohammed, Hussein, Samar Ahmed, Abdallah, Engy, Nada, Yassmin Mohamed, Kamal, Rasha, Mohamed, Ghada, Taha, Sherif Nasser, Moustafa, Amr Farouk Ibrahim
Předmět:	PREDICTIVE tests READING BIOPSY RISK assessment REFERENCE values RECEIVER operating characteristic curves DIAGNOSTIC imaging DATA analysis BREAST tumors EARLY detection of cancer ARTIFICIAL intelligence RESEARCH evaluation PROBABILITY theory KRUSKAL-Wallis Test DECISION making in clinical medicine DESCRIPTIVE statistics CHI-squared test LONGITUDINAL method WORKFLOW MAMMOGRAMS COMPUTERS in medicine STATISTICS DIGITAL image processing DATA analysis software SENSITIVITY & specificity (Statistics) PATIENT aftercare
Zdroj:	Egyptian Journal of Radiology & Nuclear Medicine; 9/12/2024, Vol. 55 Issue 1, p1-14, 14p
Abstrakt:	Background: Although artificial intelligence (AI) has potential in the field of screening of breast cancer, there are still issues. It is vital to make sure AI does not overlook cancer or cause needless recalls. The aim of this work was to investigate the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening. The study prospectively analyzed 32,822 screening mammograms. Reading was performed in a blind-paired style by (i) two radiologists and (ii) one radiologist paired with AI. A heatmap and abnormality scoring percentage were provided by AI for abnormalities detected on mammograms. Negative mammograms and benign-looking lesions that were not biopsied were confirmed by a 2-year follow-up. Results: Double reading by the radiologist and AI detected 1324 cancers (6.4%); on the other side, reading by two radiologists revealed 1293 cancers (6.2%) and presented a relative proportion of 1·02 (p < 0·0001). At the recall stage, suspicion and biopsy recommendation were more presented by the AI plus one radiologist combination than by the two radiologists. The interpretation of the mammogram by AI plus only one radiologist showed a sensitivity of 94.03%, a specificity of 99.75%, a positive predictive value of 96.571%, a negative predictive value of 99.567%, and an accuracy of 99.369% (from 99.252 to 99.472%). The positive likelihood ratio was 387.260, negative likelihood ratio was 0.060, and AUC "area under the curve" was 0.969 (0.967–0.971). Conclusions: AI could be used as an initial reader for the evaluation of screening mammography in routine workflow. Implementation of AI enhanced the opportunity to reduce false negative cases and supported the decision to recall or biopsy. [ABSTRACT FROM AUTHOR]
Databáze:	Supplemental Index
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