Unsupervised detection of potentially necrotic intestinal segments using autoencoder residuals and multispectral imaging.

Accurate and timely identification of potentially necrotic intestinal segments during surgery is critical for surgical decision-making. However, existing classification methods heavily rely on labeled data or struggle to capture early subtle abnormal features, thereby limiting their accuracy and generalizability in clinical applications. This study aims to achieve sensitive detection of potentially necrotic intestinal segments through an unsupervised one-class classification method based on autoencoder (AE) residuals. The AE was trained using readily available multispectral data from the normal small intestine to construct residuals that amplify abnormal spectral differences. The residual features were used as input for three unsupervised one-class classification algorithms, namely Local Outlier Factor, Isolation Forest and Minimum Covariance Determinant (MCD) for detection. Validation data were collected from rabbit models under different occlusion durations. The construction of residuals significantly improved the performance of all classifiers. In the potentially necrotic phase at 10 min of occlusion, the overall accuracy of the algorithms was steadily improved at about 80%, especially the sensitivity of MCD jumped from 26.5% to 80.7%, ensuring high detection rates from the outset. When the occlusion duration was extended, the sensitivity of all algorithms showed a positive correlation, consistent with the object physiological patterns. Final visualization results confirmed that residual features improved classification performance and preserved spatial continuity. This approach shows promise for early anomaly detection in other medical imaging applications.