Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks

Längkvist, Martin; Jendeberg, Johan; Thunberg, Per; Loutfi, Amy; Lidén, Mats

doi:10.1016/j.compbiomed.2018.04.021

Cited by 65 publications

(32 citation statements)

References 14 publications

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“…In recent years, a large number of studies have been performed using machine learning in radiology in many different applications including neuroimaging and imaging of the chest and abdomen, where the main interest has been towards oncology imaging and anatomy [12]. In two previous studies, CNNs have been used for the detection of ureteral stones [16,17]. We are not aware of any study previously published on the differentiation between lower ureteral stones and pelvic phleboliths on NECT using CNN.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

2020

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The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cutoff values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination. Keywords Computed tomography • Ureteral calculi • Pelvic phlebolith • Deep learning • Convolutional neural networks Abbreviations NECT Non-contrast-enhanced computed tomography CTU CT urography CNN Convolutional neural network 2.5D-CNN 2.5-Dimensional CNN including axial, coronal and sagittal images CAD Computer-aided diagnosis ANN Artificial neural network AI Artificial intelligence FC layer Fully connected layer PACS Picture archiving and communication system MPR Multiplanar reconstructions CI Confidence interval ROC Receiver operating characteristic AUC Area under the curve ROI Region of interest Key points A Convolutional Neural Network for classifying pelvic calcifications was developed and validated.

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Section: Discussionmentioning

confidence: 99%

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

2020

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“…Various AI algorithms such as the Bayesian model, decision trees, ANNs, and rule-based classifiers were used in this system to understand the complex biological features involved in predicting kidney stones, with the system yielding an accuracy of 97.1%. Längkvist et al [ 31 ] built a CNN (convolutional neural network) model for the detection of ureteral stones in high-resolution CT scans. This model was able to classify stones with a specificity of 100%, where the false positive was found to be 2.68 per scan and the AUC–ROC (receiver operating characteristic curve) was 0.9971.…”

Section: Diagnosismentioning

confidence: 99%

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature

Hameed

Dhavileswarapu

Raza

et al. 2021

JCM

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Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In this article, we aim to discuss how algorithms and techniques of artificial intelligence are equipped in the field of urology to detect, treat, and estimate the outcomes of urological diseases. Furthermore, we explain the advantages that come from using AI over any existing traditional methods.

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“…Ten studies evaluated the role of AI in KSD imaging for the diagnosis of stone disease. Langkvist et al [5] used a deep learning convolutional neural network (DCNN) to distinguish ureteric stones from phleboliths based on the thin-slice CT images from the database of 465 patients. The model was tested on 88 scan images.…”

Section: Imaging Of Ksdmentioning

confidence: 99%

“…The model was tested on 88 scan images. The results showed a sensitivity of 100% with a mean false positive rate of 2.68 per patient [5]. Parakh et al studied the diagnostic performance of the CNN on CT images for detection of urinary stones in 535 adult patients assumed to have renal calculi using two scanners.…”

Section: Imaging Of Ksdmentioning

confidence: 99%

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

et al. 2021

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Purpose of Review To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Recent Findings This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. Summary The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

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Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks

Cited by 65 publications

References 14 publications

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

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