Comparative analysis of deep learning model artificial intelligence and radiologists in breast tumor classification: A study in Uzbekistan.

e13650 Background: To evaluate and compare the diagnostic performance of a deep learning-based artificial intelligence (AI) system versus three radiologists in the detection of breast cancer using digital mammography, specifically within the context of Uzbekistan, and to determine if AI can serve as a reliable tool in this setting. Methods: This retrospective study utilized a dataset of mammograms, sourced from Uzbekistan, which were independently assessed by three radiologists and an AI system. The AI model, based on deep neural networks, was designed for automated breast cancer detection. The radiologists’ interpretations and the AI predictions were compared against a reference standard of biopsy results. The primary outcome measures included the area under the receiver operating characteristic curve (AUC), accuracy, and specificity for both the AI system and radiologists. The data underwent rigorous statistical analysis to establish the significance of the observed differences. The model was trained using data from multiple institutions in multiple countries. Results: The AI system demonstrated a significantly higher area under the curve (AUC of 0.89) compared to the average of three radiologists (AUC of 0.82). The AI also showed higher specificity (e.g., 93.0% versus 77.6%), and the recall rate for AI was three times lower than that of radiologists. The AI was more sensitive in detecting cancers with mass, distortion, or asymmetry and better at detecting T1 or node-negative cancers. This result underscores AI's potential to reduce false positives, but also demonstrates that it can detect cancers missed by radiologists. The AI system's performance aligns with other studies showing AI sensitivity to be non-inferior to, or surpassing, radiologists. AI systems can detect more cancers with mass or distortion than radiologists. The statistical analysis showed that the AI system achieved robust accuracy and demonstrated potential as a reliable tool to enhance breast cancer screening outcomes. A study also showed that AI can reduce the number of reads in a screening program by 41.4%. Conclusions: In this study the AI system outperformed the group of radiologists in terms of AUC, specificity, recall rates, and positive predictive value. These findings suggest that deep learning-based AI can significantly improve the detection of breast cancer in mammography and may serve as a valuable tool in the Uzbekistan healthcare setting. Additional studies that include larger, more heterogenous datasets are warranted and it is important to continue researching AI integration, including risk management and real-world follow up of performance. Future studies should examine the impact of AI on screening performance when used by radiologists and assess the value of different models for various conditions.

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