An Outperforming Artificial Intelligence Model to Identify Referable Blepharoptosis for General Practitioners
此外，為了可視化合理的AI預測，Grad-CAM還有助於識別圖像中的數據集偏差。例如，眼部周圍的術前標記或眼瞼術後縫合可能會為AI模型提供誤導性線索，而不是眼瞼下垂的眼瞼信息。Grad-CAM 的結果（如附圖）顯示了上眼瞼邊緣和中央角膜光反射之間的熱點區域（重量為 0.5-1.0），這在臨床上與 MRD-1 概念兼容。背景中的冷區（權重為 0-0.2）成功排除了數據集偏差，提供了更強的忠實度。隨著未來數據使用量的增加和多樣化，可以期待更精確的結果來理解人工智能的預測。
Blepharoptosis, also known as ptosis, is the drooping or inferior displacement of the upper eyelid. Ptosis can obstruct the visual axis and affect vision. It can be a presenting sign of a severe medical disorder, such as ocular myasthenia , third cranial nerve palsy , or Horner syndrome . It is essential for general practitioners to accurately diagnose ptosis to assist in decision-making for referral and work up when necessary.
With low repeatability and reproducibility in measuring eyelid landmarks and the effect of learning curves [5,6], accurately recognizing ptosis is challenging for non-ophthalmologists. As a result, Professor Chiou-Shann Fuh and his team developed an artificial intelligence model that automatically identifies referable blepharoptosis as an automated tool to assist general practitioners in diagnosing ptosis.
In this study, Fuh’s team used VGG-16 as the base structure to train an AI model whose training purpose is to diagnose ptosis accurately. The last few layers of VGG-160s architecture were replaced with a global max pooling layer followed by fully connected layers and a sigmoid function for the binary classification problem. This model also performs transfer learning by importing weights trained on ImageNet and applying data augmentation to prevent overfitting. On the other hand, three specialists, one in emergency medicine, neurology, and one in family medicine, were tested on behalf of the non-ophthalmologist group. For the AI model and physician group, this study provided the same test set for both sides, including 25 healthy eyelids and 25 ptotic eyelids, to distinguish ptotic eyelids from healthy eyelids. The test showed that the accuracy of the AI model was 90%, with a sensitivity of 92% and a specificity of 88%; the accuracy of the physician group model was 77.33%, with a sensitivity of 72% and a specificity of 82.67%. These results suggest that an AI-aided diagnostic tool can accurately detect blepharoptosis and prompt referral for ophthalmic evaluation when necessary.
In addition, to visualize reasonable AI predictions, Grad-CAM also helped identify image dataset biases. For example, a preoperative marking around the eye or a postoperative suture on the eyelid may provide misleading clues to the AI model rather than eyelid information for blepharoptosis. The results of Grad-CAM (as shown in the attached figure) demonstrated a hotspot area (0.5–1.0 in weights) between the upper eyelid margin and central corneal light reflex, which is clinically compatible with the MRD- 1 concept. The background cold zone (0–0.2 in weights) successfully excluded dataset biases, providing more vital faithfulness. With more extensive and diverse data utilization in the future, more precise results can be expected to understand AI predictions.