A novel artificial intelligence segmentation model for early diagnosis of bladder tumors.

OBJECTIVE

Despite cystoscopy plays an important role in bladder tumors diagnosis, it often falls short in flat cancerous tissue and minuscule satellite lesions. It can easily lead to a missed diagnosis by the urologist, which can lead to a swift tumor regrowth following transurethral resection of the bladder tumor (TURBT). Therefore, we developed a deep learning-based intelligent diagnosis system for early bladder cancer to improve the identification rate of early bladder tumors.

METHODS

Video data from 273 bladder cancer patients who underwent TURBT at Zhongnan Hospital were collected. The dataset was carefully annotated by urologists to clearly define tumor boundaries. Subsequently, we developed a new bladder tumor segmentation network (BTS-Net) based on transformer to accurately diagnose early-stage bladder cancer lesions.

RESULTS

Our experiments demonstrate that the BTS-Net we developed has outperformed other method on the external B validation dataset, achieving a MPrecision of 91.39%, a MRecall of 95.71%, a MIoU of 88.18% and an F1-score of 93.18%. The BTS-Net showed high accuracy with real-time processing speed at 23 fps.

CONCLUSION

Missed detection of satellite lesions in early bladder tumors often leads to tumor recurrence. Our BTS-Net is capable of segmenting all potential satellite lesions in surgical videos, without the need for complex professional equipment. This AI-assisted diagnosis system has the potential to improve surgical outcomes by ensuring comprehensive treatment of all tumor-related areas during TURBT.