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Sökning: L773:1651 2065 OR L773:2168 1805 OR L773:2168 1813 > A novel model of ar...

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FältnamnIndikatorerMetadata
00006003naa a2200565 4500
001oai:research.chalmers.se:f31dc1ed-6f8a-436a-b90f-f79c9ff0484f
003SwePub
008240614s2024 | |||||||||||000 ||eng|
009oai:lup.lub.lu.se:1b4289b2-2b79-4377-abe1-d83f5a4a98cf
009oai:DiVA.org:liu-204375
0247 a https://doi.org/10.2340/sju.v59.399302 DOI
0247 a https://research.chalmers.se/publication/5415632 URI
0247 a https://lup.lub.lu.se/record/1b4289b2-2b79-4377-abe1-d83f5a4a98cf2 URI
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2043752 URI
040 a (SwePub)cthd (SwePub)lud (SwePub)liu
041 a engb eng
042 9 SwePub
072 7a art2 swepub-publicationtype
072 7a ref2 swepub-contenttype
1001 a Abuhasanein, Suleimanu Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Univ Gothenburg, Sweden; NU Hosp Grp, Sweden4 aut
2451 0a A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria
264 1b Medical Journal Sweden AB,c 2024
338 a electronic2 rdacarrier
500 a Funding Agencies|Swedish state [ALFGBG-873181]; Department of Research; Development, NU-Hospital Group
520 a 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.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Urologi och njurmedicin0 (SwePub)302142 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Urology and Nephrology0 (SwePub)302142 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
653 a computed tomography
653 a Artificial intelligence
653 a deep learning
653 a convolutional neural networks
653 a hematuria
653 a bladder cancer
7001 a Edenbrandt, L.u Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden4 aut
7001 a Enqvist, Olof,d 1981u Chalmers University of Technology,Chalmers Univ Technol, Sweden; Eigenvision AB, Sweden,Chalmers tekniska högskola4 aut0 (Swepub:cth)enolof
7001 a Jahnson, Staffanu Linköpings universitet,Linköping University,Avdelningen för kirurgi, ortopedi och onkologi,Medicinska fakulteten,Region Östergötland, Urologiska kliniken i Östergötland4 aut0 (Swepub:liu)staja74
7001 a Leonhardt, Henrik,d 1963u Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden4 aut
7001 a Trägårdh, Elinu 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,Skånes universitetssjukhus (SUS)4 aut0 (Swepub:lu)klin-etr
7001 a Ulén, Johannesu Eigenvision AB, Sweden; Univ Gothenburg, Sweden4 aut
7001 a Kjölhede, Henriku Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Univ Gothenburg, Sweden; Sahlgrens Univ Hosp, Sweden4 aut
7102 a Göteborgs universitetb Sahlgrenska Academy4 org
7730 t Scandinavian Journal of Urologyd : Medical Journal Sweden ABg 59, s. 90-97q 59<90-97x 2168-1805x 2168-1813
8564 u https://research.chalmers.se/publication/541563/file/541563_Fulltext.pdfx primaryx freey FULLTEXT
8564 u http://dx.doi.org/10.2340/sju.v59.39930x freey FULLTEXT
8564 8u https://doi.org/10.2340/sju.v59.39930
8564 8u https://research.chalmers.se/publication/541563
8564 8u https://lup.lub.lu.se/record/1b4289b2-2b79-4377-abe1-d83f5a4a98cf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-204375

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