## 3 Results  
Three hundred and seventy-eight mammograms, radiology reports, and pathological findings were reviewed. The patients' mean age was 50.3 years (range, 43-57 years); median breast thickness, 57 mm; and median glandular dose, 6.6 mGy. Most patients had breast density B (42.3%) and a breast imaging reporting and data system (BIRADS) classification V (36.9%) or II (20.9%). Most patients diagnosed with cancer had invasive ductal carcinoma (35.5%). Of the patients, 10.05% had no evidence of malignancy and 42.06% showed no evidence of malignancy after follow-up for at least 6 months. The results presented in Figure 1 and Tables 1 and 2 were from Clearfield. Figure 2: The detection and characterization of suspicious lesions on mammograms using the MammoScreen software. The lesions are categorized into three main categories: red, highly suspicious lesions; yellow, lesions suspected of malignancy; and green, lesions with low suspicion of malignancy. The green lesions are not displayed by default on the MammoScreen interface but can be displayed using the filtering button at the bottom left corner of the interface. The AI system and radiologists were compared in terms of their overall assessment performance of the mammograms. The area under the curve (AUC) value of the AI system (0.923; 95% CI, 0.893-0.954) was higher than that of the radiology assessment (0.838; 95% CI, 0.796-0.881), as shown in Table 3. The AUC difference was 0.085, which was slightly higher for the AI system. The average ROC curves are displayed in Figure 3. As shown in Tables 4 and 5, the AI system agrees with the ground truth results to diagnose 167 cases (91.3%) as positive findings and 182 cases (93.3%) as negative findings. On the other hand, the AI system does not agree with the ground results that 16 negative results (8.7%) in the histopathological analysis of biopsy samples were positive results (false-positive results). Furthermore, 13 positive results (6.7%) in the histopathological analysis of biopsy samples were diagnosed as negative results with the AI system (false-negative results). The sensitivity, specificity, PPV, NPV, and accuracy of the AI system were 92.8%, 91.9%, 91.3%, 93.3%, and 92.3%, respectively. \begin{table}
\begin{tabular}{c c} \hline
**Characteristic** & **Value** \\ \hline Age (y), mean \(\pm\) SD & 50.31 \(\pm\) 10.5 \\ Breast thickness (mm), median (interquartile range) & 57.0 (48–66) \\ Glandular dose (mGy), median (interquartile range) & 6.6 (4.7–10.4) \\ Breast density category, \(n\) (\%) & 103 (27.2) \\ B & 160 (42.3) \\ C & 98 (25.9) \\ D & 17 (4.5) \\ BI-RADS categories & \\
0 & 34 (9.0) \\ I & 21 (5.6) \\ II & 79 (20.9) \\ III & 38 (10.1) \\ IV & 70 (18.5) \\ V & 136 (36.9) \\ Mean MammoScreen score, median (interquartile range) & 6.03 (4–9) \\ \hline BI-RADS = Breast imaging reporting and data system. & \\ \end{tabular}
\end{table} 
Table 1: Demographic and clinical characteristics of the study population and selected digital mammograms (\(n\) = 378). \begin{table}
\begin{tabular}{c c} \hline
**Histological Type** & **No. of Cases, \(n\) (\%)** \\ \hline Invasive ductal carcinoma & 141 (37.3) \\ \hline Metastatic carcinoma & 12 (3.17) \\ \hline Ductal carcinoma in situ & 10 (2.6) \\ \hline Invasive lobular carcinoma & 7 (1.85) \\ \hline Other types & 11 (2.91) \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the patients with breast cancer in the selected dataset (\(n\) = 181). \begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **AUC** & \(p\)**-Value** & **95\% CI** \\ \hline Artificial intelligence system & & \\ assessment results & 0.923 & 0.001 & 0.893–0.954 \\ \hline Radiologists’ assessment results & 0.838 & 0.001 & 0.796–0.881 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of AUC values between the AI system and radiologists’ assessment results. As shown in Tables 4 and 5, the radiologists' assessment results agree with the ground truth results to diagnose 180 cases (73.8%) as positive findings and 134 cases (100%) as negative findings.