To further analyze the performance and effectiveness of the AI models, 4 evaluation indicators were calculated after 500 fused pictures were classified. The four evaluation indicators include true-positive (TP) numbers, false-positive (FP) number, true-negative (TN), and false-negative (FN) numbers. TP stands for the samples that are correctly classified as good quality. FP means that samples of poor quality are mistakenly classified as of good quality. TN represents those who are correctly classified as poor quality. FN is the number of samples that belong to good quality but are somehow classified as poor quality. Among these 500 pictures, the number of TP and TN was as high as 223 and 214. The FP and FN were 2 and 12, respectively. These results indicated that the AI-assisted image classification tool was useful and practical. Since the 'machine' can classify images to good quality and poor quality, researchers can apply this methodology to cope with blurred electrochemical images or apply this technique to bioimaging assays.