Sökning: L773:1651 2065 OR L773:2168 1805 OR L773:2168 1813 > A novel model of ar...
Fältnamn | Indikatorer | Metadata |
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000 | 06003naa a2200565 4500 | |
001 | oai:research.chalmers.se:f31dc1ed-6f8a-436a-b90f-f79c9ff0484f | |
003 | SwePub | |
008 | 240614s2024 | |||||||||||000 ||eng| | |
009 | oai:lup.lub.lu.se:1b4289b2-2b79-4377-abe1-d83f5a4a98cf | |
009 | oai:DiVA.org:liu-204375 | |
024 | 7 | a https://doi.org/10.2340/sju.v59.399302 DOI |
024 | 7 | a https://research.chalmers.se/publication/5415632 URI |
024 | 7 | a https://lup.lub.lu.se/record/1b4289b2-2b79-4377-abe1-d83f5a4a98cf2 URI |
024 | 7 | 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 | 7 | a art2 swepub-publicationtype |
072 | 7 | a ref2 swepub-contenttype |
100 | 1 | a Abuhasanein, Suleimanu Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Univ Gothenburg, Sweden; NU Hosp Grp, Sweden4 aut |
245 | 1 0 | a A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria |
264 | 1 | b 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 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Urologi och njurmedicin0 (SwePub)302142 hsv//swe |
650 | 7 | a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Urology and Nephrology0 (SwePub)302142 hsv//eng |
650 | 7 | a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe |
650 | 7 | a 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 | |
700 | 1 | a Edenbrandt, L.u Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden4 aut |
700 | 1 | a Enqvist, Olof,d 1981u Chalmers University of Technology,Chalmers Univ Technol, Sweden; Eigenvision AB, Sweden,Chalmers tekniska högskola4 aut0 (Swepub:cth)enolof |
700 | 1 | 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 |
700 | 1 | a Leonhardt, Henrik,d 1963u Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Sahlgrens Univ Hosp, Sweden; Univ Gothenburg, Sweden4 aut |
700 | 1 | 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 |
700 | 1 | a Ulén, Johannesu Eigenvision AB, Sweden; Univ Gothenburg, Sweden4 aut |
700 | 1 | a Kjölhede, Henriku Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Univ Gothenburg, Sweden; Sahlgrens Univ Hosp, Sweden4 aut |
710 | 2 | a Göteborgs universitetb Sahlgrenska Academy4 org |
773 | 0 | t Scandinavian Journal of Urologyd : Medical Journal Sweden ABg 59, s. 90-97q 59<90-97x 2168-1805x 2168-1813 |
856 | 4 | u https://research.chalmers.se/publication/541563/file/541563_Fulltext.pdfx primaryx freey FULLTEXT |
856 | 4 | u http://dx.doi.org/10.2340/sju.v59.39930x freey FULLTEXT |
856 | 4 8 | u https://doi.org/10.2340/sju.v59.39930 |
856 | 4 8 | u https://research.chalmers.se/publication/541563 |
856 | 4 8 | u https://lup.lub.lu.se/record/1b4289b2-2b79-4377-abe1-d83f5a4a98cf |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-204375 |
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