A quantitative image analysis using MRI for diagnosis of biliary atresia

Lin, Dao Chen; Wu, Kun; Sun, Fang; Huang, Chun; Wu, Tung Hsin; Shih, Shou-Chuan; Tsai, Pei Shan

doi:10.1016/j.clinimag.2018.10.001

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…As a result, the diagnostic process is prolonged and requires a significant period of post-procedural care ( 13 ). Although several diagnostic factors and prediction models have been considered for effective diagnosis of BA, they either have lacked satisfactory sensitivity and specificity or are not readily applicable or accessible in the hospital setting ( 14 – 17 ).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

et al. 2020

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Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN). Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness. Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%. Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

et al. 2020

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“…Moreover, the isotopes for the examination are no longer available in most regions and countries. Magnetic resonance imaging (MRI) presents good sensitivity and specificity of up to 98% [ 8 ]. However, the procedure requires sedation, and MRI equipment with better accuracy of infant images is not common in local hospitals.…”

Section: Introductionmentioning

confidence: 99%

The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models

Hsu

Dai²,

Chou³

et al. 2022

PLoS ONE

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Purpose Early confirmation or ruling out biliary atresia (BA) is essential for infants with delayed onset of jaundice. In the current practice, percutaneous liver biopsy and intraoperative cholangiography (IOC) remain the golden standards for diagnosis. In Taiwan, the diagnostic methods are invasive and can only be performed in selective medical centers. However, referrals from primary physicians and local pediatricians are often delayed because of lacking clinical suspicions. Ultrasounds (US) are common screening tools in local hospitals and clinics, but the pediatric hepatobiliary US particularly requires well-trained imaging personnel. The meaningful comprehension of US is highly dependent on individual experience. For screening BA through human observation on US images, the reported sensitivity and specificity were achieved by pediatric radiologists, pediatric hepatobiliary experts, or pediatric surgeons. Therefore, this research developed a tool based on deep learning models for screening BA to assist pediatric US image reading by general physicians and pediatricians. Methods De-identified hepatobiliary US images of 180 patients from Taichung Veterans General Hospital were retrospectively collected under the approval of the Institutional Review Board. Herein, the top network models of ImageNet Large Scale Visual Recognition Competition and other network models commonly used for US image recognition were included for further study to classify US images as BA or non-BA. The performance of different network models was expressed by the confusion matrix and receiver operating characteristic curve. There were two methods proposed to solve disagreement by US image classification of a single patient. The first and second methods were the positive-dominance law and threshold law. During the study, the US images of three successive patients suspected to have BA were classified by the trained models. Results Among all included patients contributing US images, 41 patients were diagnosed with BA by surgical intervention and 139 patients were either healthy controls or had non-BA diagnoses. In this study, a total of 1,976 original US images were enrolled. Among them, 417 and 1,559 raw images were from patients with BA and without BA, respectively. Meanwhile, ShuffleNet achieved the highest accuracy of 90.56% using the same training parameters as compared with other network models. The sensitivity and specificity were 67.83% and 96.76%, respectively. In addition, the undesired false-negative prediction was prevented by applying positive-dominance law to interpret different images of a single patient with an acceptable false-positive rate, which was 13.64%. For the three consecutive patients with delayed obstructive jaundice with IOC confirmed diagnoses, ShuffleNet achieved accurate diagnoses in two patients. Conclusion The current study provides a screening tool for identifying possible BA by hepatobiliary US images. The method was not designed to replace liver biopsy or IOC, but to decrease human error for interpretations of US. By applying the positive-dominance law to ShuffleNet, the false-negative rate and the specificities were 0 and 86.36%, respectively. The trained deep learning models could aid physicians other than pediatric surgeons, pediatric gastroenterologists, or pediatric radiologists, to prevent misreading pediatric hepatobiliary US images. The current artificial intelligence (AI) tool is helpful for screening BA in the real world.

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“…Abnormal gallbladder shape, triangular cord sign, and hepatic artery enlargement are the main kjronline.org ultrasound discriminators of BA; however, the results are overlapping (6,7). The assessment of extra-hepatic bile ducts can be done by magnetic resonance (MR) or intraoperative cholangiopancreatography (8,9). Few studies have discussed the role of ultrasound in the differentiation of BA from ALGS because both present early with neonatal cholestasis (10,11).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Diffusion Tensor Imaging Parameters of Hepatic Parenchyma for Differentiation of Biliary Atresia from Alagille Syndrome

et al. 2020

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Objective: To assess diffusion tensor imaging (DTI) parameters of the hepatic parenchyma for the differentiation of biliary atresia (BA) from Alagille syndrome (ALGS). Materials and Methods: This study included 32 infants with BA and 12 infants with ALGS groups who had undergone DTI. Fractional anisotropy (FA) and mean diffusivity (MD) of the liver were calculated twice by two separate readers and hepatic tissue was biopsied. Statistical analyses were performed to determine the mean values of the two groups. The optimum cutoff values for DTI differentiation of BA and ALGS were calculated by receiver operating characteristic (ROC) analysis. Results: The mean hepatic MD of BA (1.56 ± 0.20 and 1.63 ± 0.2 x 10-3 mm 2 /s) was significantly lower than that of ALGS (1.84 ± 0.04 and 1.79 ± 0.03 x 10-3 mm 2 /s) for both readers (r = 0.8, p = 0.001). Hepatic MD values of 1.77 and 1.79 x 10-3 mm 2 /s as a threshold for differentiating BA from ALGS showed accuracies of 82 and 79% and area under the curves (AUCs) of 0.90 and 0.91 for both readers, respectively. The mean hepatic FA of BA (0.34 ± 0.04 and 0.36 ± 0.04) was significantly higher (p = 0.01, 0.02) than that of ALGS (0.30 ± 0.06 and 0.31 ± 0.05) for both readers (r = 0.80, p = 0.001). FA values of 0.30 and 0.28 as a threshold for differentiating BA from ALGS showed accuracies of 75% and 82% and AUCs of 0.69 and 0.68 for both readers, respectively. Conclusion: Hepatic DTI parameters are promising quantitative imaging parameters for the detection of hepatic parenchymal changes in BA and ALGS and may be an additional noninvasive imaging tool for the differentiation of BA from ALGS.

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A quantitative image analysis using MRI for diagnosis of biliary atresia

Cited by 6 publications

References 22 publications

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models

Assessment of Diffusion Tensor Imaging Parameters of Hepatic Parenchyma for Differentiation of Biliary Atresia from Alagille Syndrome

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