Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence

Augmented Bladder Tumor Detection Using the Bladder-Portable Artifact Detection System: A Multicentric Prospective Analytic Study Using Real Time Based Artificial Intelligence (IA).

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05415631

Acronym

Bladder-PAD

Enrollment

500

Registered

2022-06-13

Start date

2022-05-13

Completion date

2029-05-31

Last updated

2022-07-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Bladder Cancer, Cystoscopy

Keywords

Bladder Cancer, cystoscopy, Artificial Intelligence

Brief summary

Today the standard for the diagnosis and monitoring of bladder tumors is bladder endoscopy. The performance of this exam is not perfect. With this work, based on artificial intelligence, the investigators wish to combine endoscopy with a complementary diagnostic tool in order to improve patient care. The main objective will be to reduce diagnostic errors / wanderings in patients treated or followed for bladder tumors, by imposing a new standard of diagnostic bladder mapping (high PPV and VPN, high precision)(primary purpose diagnostic). The secondary objective will be to homogenize and systematize the descriptive part of the lesions, and to use AI to better characterize tumor aggressiveness. The final objective being to validate a new precision tool (diagnostic companion) essential for developing and standardizing the therapeutic management of bladder tumors (correcting inter-observer heterogeneity). In this project, video frame will be first extracted from our dataset of cystoscopy videos hosted in in the Next Cloud Recherche. Selected medical image will be segmented and analyzed using our pre-trained CNN model with a feature detection algorithm to obtain features. Data will be analyzed on both patient and lesion levels. The study will assess the Bladder-PAD accuracy on the detection of bladder tumors, and its ability to predict tumor risk of recurrence and progression.

Interventions

None listed

Study design

Observational model

CASE_ONLY

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

* unifocal primary or recurrent suspected bladder cancer with tumor size less or equal than 3 cm * multifocal primary or recurrent suspected bladder cancer less or equal than 5 lesions and with tumor size less or equal than 3 cm.

Exclusion criteria

* Evidence of more than 5 tumors or more than 3 cm * computed tomography/cystoscopy suspect of muscle-invasive bladder cancer (cT2 or higher) * computed tomography/magnetic resonance evidence of distant metastases (lymphatic or organic) *

Design outcomes

Primary

Measure	Time frame
Tumor detection rate of white light cystoscopy	one day
Tumor detection rate of Bladder-PAD cystoscopy	one day
Tumor false detection rate of white light cystoscopy	one day
Tumor false detection rate of Bladder-PAD cystoscopy	one day

Countries

France

Contacts

Primary ContactFabien SAINT, Pr

saint.fabien@chu-amiens.fr03 22 45 59 45

Outcome results