We read with great interest the prospective study by Morales et al,1 which demonstrates that the retrocervical sliding sign on transvaginal ultrasound has high specificity (96%) and negative predictive value (96%) for detecting pouch of Douglas obliteration, and that a negative sign more than doubles operative time (139 vs. 60 min). These findings support the routine use of this simple maneuver in preoperative planning. However, the study's conclusion that “transvaginal ultrasound, particularly the sliding sign, is a reliable tool for excluding pelvic adhesions” may be overly broad, as the high sliding sign performed poorly (sensitivity 18.2%) and would be unreliable for ruling out fundal adhesions in clinical practice. The authors correctly note the limited utility of the high sliding sign, yet the abstract and conclusion do not explicitly warn against its use. Given that busy clinicians may only read the abstract, we suggest that future guidelines clearly differentiate between the retrocervical sliding sign (recommended) and the high sliding sign (not recommended for ruling out adhesions). This distinction is critical to avoid false reassurance when the high sliding sign is positive. Beyond this clinical message, we believe the study could be extended by applying advanced machine learning techniques to improve the sensitivity of sliding sign assessment. For instance, temporal attention mechanisms2 have been effective in weighting later, more clinically relevant observations in medical time series; adapting such an approach to sequential ultrasound frames might emphasize the most informative phases of organ movement. Similarly, residual neural networks3 have been successfully used to capture complex feature interactions, suggesting that deeper feature extraction from ultrasound cine loops could improve the discrimination of minimal adhesions. Moreover, singular pooling4 has been shown to preserve fine-grained spatial information from sparse feature maps in ultrasound image classification, which may enhance the detection of subtle mobility restrictions. Finally, the ALEFB framework5 demonstrates how adaptive gated feature fusion and lightweight design can improve discrimination of anatomically similar structures under real-time constraints; this strategy could be applied to distinguish subtle differences between normal sliding and restricted mobility caused by loose adhesions. We do not intend to diminish the value of the authors' work—their prospective, blinded design is exemplary. Rather, we hope these observations encourage further refinement of ultrasound-based adhesion assessment through both clearer clinical messaging and the integration of modern representation learning techniques. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
Jiaxin Cai (Sat,) studied this question.