Objectives Bladder cancer is one of the most common malignancies of the urinary system, and early diagnosis and treatment are crucial for improving patient prognosis. Methods In this study, we developed an artificial intelligence (AI) model for bladder cancer diagnosis using CT imaging data from our hospital and The Cancer Imaging Archive (TCIA).
The model was trained and validated using a large dataset of CT images, and its diagnostic accuracy was assessed through various performance met
class had shown consistent performance, while the bladder and bladder cancer classes exhibited slight variations between the two datas - ets. This may suggest that the model encounters certain challenges when identifying the bladder and bladder cancer, or that the validation set contains more challenging samples.
As for accuracy, the background class still maintains a very high level, and the accu - racy of the bladder and bladder cancer classes has increased in the test set. This further confirms the potential issues the model faces when processing these two classes.Page 6 of 12 Jiang et
In this study, we developed an artificial intelligence (AI) model for bladder cancer diagnosis using CT imaging
were reviewed by radiologists. Results The test–set accuracy exceeded 0.90, and Grad—CAM correctly highlighted tumors. Conclusions The AI model offers accurate and transparent bladder—cancer detection on routine CT scans and merits prospective validation. Keywords Artificial intelligence, Bladder cancer, Diagnosis Page 2 of 12 Jiang et al.
Discover Oncology (2026) 17:140 SARS-CoV-2-driven phosphorylation site predictions overlap with lung cancer progres - sion networks, highlighting the need to reconcile biological prior knowledge with black- box AI predictions to guide precise therapy [ 5, 6]. Extending this paradigm to bladder cancer, Grad-CAM offers a vehicle to bridge m
Bladder cancer remains a prevalent malignancy affecting the urinary system, with a ris - ing incidence globally [ 1]. This disease significantly impacts patients’ quality of life and poses considerable economic burdens due to high treatment costs [ 1, 2].
Traditional diagnostic approaches, such as cystoscopy and biopsy, are invasive and time-consuming, often leading to patient discomfort and potential complications [ 31, 32]. Consequently, the need for more efficient and non-invasive diagnostic methods is urgent to enhance early detection and improve patient outcomes [ 33, 34].
Advances in imaging technolo - gies and AI present promising avenues for the development of innovative diagno
s The AI model offers accurate and transparent bladder—cancer detection on routine CT scans and merits prospective validation. Keywords Artificial intelligence, Bladder cancer, Diagnosis Page 2 of 12 Jiang et al.
Discover Oncology (2026) 17:140 SARS-CoV-2-driven phosphorylation site predictions overlap with lung cancer progres - sion networks, highlighting the need to reconcile biological prior knowledge with black- box AI predictions to guide precise therapy [ 5, 6]. Extending thi