Aesthetic outcome of reconstructed breasts is currently rated subjectively by plastic surgeons, which introduces inter-rater bias and variability; thus, an image-based objective technique is desired. Developing such a technique, however, has been challenging due to the limited availability of reconstructed breast images under standardized conditions, and the complexity of assessing multiple aesthetic viewpoints. In this study, we propose a clinically-oriented two-dimensional (2D) image-analysis system where small fingerprint pairs representing the left and right breasts are extracted from conventional 2D chest images obtained in clinical settings and used as training data for a simple convolutional neural network (CNN), aiming for high effectiveness even with a limited number of cases. We extracted 16 type variations of fingerprints from 170 cases, and evaluated their influence on CNN performance. The optimal fingerprint types varied depending on the aesthetic viewpoint. The overall aesthetic score, calculated by aggregating the best-performing model scores across all viewpoints, showed a strong correlation (r > 0.9) with the average rater scores. Although 2D images capture only partial breast appearances and may not fully represent intrinsic three-dimensional (3D) features, the experimental results strongly support the potential of the proposed system for developing appearance-based models for aesthetic evaluation in clinical settings.