Sökning: L773:2168 1805 OR L773:2168 1813 >
A novel model of ar...
A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria
-
- Abuhasanein, Suleiman (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för urologi,Institute of Clinical Sciences, Department of Urology,Sahlgrenska Academy,Univ Gothenburg, Sweden; NU Hosp Grp, Sweden
-
- Edenbrandt, Lars, 1957 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Sahlgrenska University Hospital,Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden
-
- Enqvist, Olof, 1981 (författare)
- Chalmers University of Technology,Chalmers Univ Technol, Sweden; Eigenvision AB, Sweden
-
visa fler...
-
- Jahnson, Staffan (författare)
- Linköpings universitet,Linköping University,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Region Östergötland, Urologiska kliniken i Östergötland
-
- Leonhardt, Henrik, 1963 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för radiologi,Institute of Clinical Sciences, Department of Radiology,Sahlgrenska Academy,Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden
-
- Trägårdh, Elin (författare)
- Lund University,Lunds universitet,Institutionen för translationell medicin,Medicinska fakulteten,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Department of Translational Medicine,Faculty of Medicine,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital,Lund Univ, Sweden; Skane Univ Hosp, Sweden
-
- Ulén, Johannes (författare)
- Eigenvision AB, Sweden; Univ Gothenburg, Sweden
-
- Kjölhede, Henrik, 1981 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för urologi,Institute of Clinical Sciences, Department of Urology,Sahlgrenska Academy,Univ Gothenburg, Sweden; Sahlgrens Univ Hosp, Sweden
-
visa färre...
-
(creator_code:org_t)
- Medical Journal Sweden AB, 2024
- 2024
- Engelska.
-
Ingår i: Scandinavian Journal of Urology. - : Medical Journal Sweden AB. - 2168-1805 .- 2168-1813. ; 59, s. 90-97
- Relaterad länk:
-
https://research.cha... (primary) (free)
-
visa fler...
-
http://dx.doi.org/10... (free)
-
https://doi.org/10.2...
-
https://research.cha...
-
https://gup.ub.gu.se...
-
https://lup.lub.lu.s...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Urologi och njurmedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Urology and Nephrology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- computed tomography
- Artificial intelligence
- deep learning
- convolutional neural networks
- hematuria
- bladder cancer
- Artificial intelligence
- bladder cancer
- computed tomography
- convolutional neural networks
- deep learning
- hematuria
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas