A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria
Artikel i vetenskaplig tidskrift, 2024

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

computed tomography

Artificial intelligence

deep learning

convolutional neural networks


bladder cancer


Suleiman Abuhasanein

Göteborgs universitet

L. Edenbrandt

Sahlgrenska universitetssjukhuset

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Staffan Jahnson

Linköpings universitet

Henrik Leonhardt

Göteborgs universitet

E. Tragardh

Skånes universitetssjukhus (SUS)

Johannes Ulén

Eigenvision AB

Henrik Kjölhede

Göteborgs universitet

Scandinavian Journal of Urology

2168-1805 (ISSN) 2168-1813 (eISSN)

Vol. 59 90-97


Urologi och njurmedicin

Radiologi och bildbehandling





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