Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening

Sakai, Akira; Komatsu, Masaaki; Komatsu, Reina; Matsuoka, Ryu; Yasutomi, Suguru; Dozen, Ai; Shozu, Kanto; Arakaki, Tatsuya; Machino, Hidenori; Asada, Ken; Kaneko, Syuzo; Sekizawa, Akihiko; Hamamoto, Ryuji

doi:10.3390/biomedicines10030551

Cited by 30 publications

(29 citation statements)

References 74 publications

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“…In other words, the expert fetal cardiologist’s interpretation performance undershot CNN’s predictions ( Figure 7 ). The results obtained following previous research using the DL method from fetal US images can help increase the detection of fetal heart abnormalities compared to human experts [ 10 , 17 , 18 , 24 ]. Correspondingly, our DL model can support the expert fetal cardiologist’s interpretation of fetal echocardiography US for making diagnoses.…”

Section: Resultsmentioning

confidence: 68%

“…The automatic analysis of fetal heart anatomy contributes significantly to the early diagnosis of CHD and the preparation for further therapy. The most successful applications of DL in fetal ultrasounds have been in pre-diagnostic tasks, including standard plane detection [ 10 ], the classification and detection of CHDs [ 13 , 14 , 15 , 16 , 17 , 18 ], and fetal heart developmental assessment [ 19 , 20 ]. We hypothesized that DL could improve the ultrasound analysis of CHDs.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases

Nurmaini

Partan²,

Bernolian³

et al. 2022

JCM

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Early prenatal screening with an ultrasound (US) can significantly lower newborn mortality caused by congenital heart diseases (CHDs). However, the need for expertise in fetal cardiologists and the high volume of screening cases limit the practically achievable detection rates. Hence, automated prenatal screening to support clinicians is desirable. This paper presents and analyses potential deep learning (DL) techniques to diagnose CHDs in fetal USs. Four convolutional neural network architectures were compared to select the best classifier with satisfactory results. Hence, dense convolutional network (DenseNet) 201 architecture was selected for the classification of seven CHDs, such as ventricular septal defect, atrial septal defect, atrioventricular septal defect, Ebstein’s anomaly, tetralogy of Fallot, transposition of great arteries, hypoplastic left heart syndrome, and a normal control. The sensitivity, specificity, and accuracy of the DenseNet201 model were 100%, 100%, and 100%, respectively, for the intra-patient scenario and 99%, 97%, and 98%, respectively, for the inter-patient scenario. We used the intra-patient DL prediction model to validate our proposed model against the prediction results of three expert fetal cardiologists. The proposed model produces a satisfactory result, which means that our model can support expert fetal cardiologists to interpret the decision to improve CHD diagnostics. This work represents a step toward the goal of assisting front-line sonographers with CHD diagnoses at the population level.

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Section: Resultsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases

Nurmaini

Partan²,

Bernolian³

et al. 2022

JCM

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“…That poses an intriguing question: how can you trust a model’s decisions if you cannot fully justify how it got there? There has been the latest trend in the growth of XAI for a better understanding of the AI black boxes [ 49 , 136 , 137 , 138 , 139 ]. Grad-CAM or Grad-CAM++ produces a coarse clustering map showing the key regions in the picture for predicting any target idea (say, “COVID-19” in a classification network) by using the gradients of any target concept (say, “COVID-19” in a classification network) in the final convolutional layer.…”

Section: Discussionmentioning

confidence: 99%

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Suri¹,

Agarwal²,

Chabert

et al. 2022

Diagnostics

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Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

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“…This problem makes it tedious to construct trust with those medical professionals. Nevertheless, visualizing the deep neural network (DNN) internal representation would maximize explanatory capability and improvise the medical professional's confidence level in AI decisions [20]. Sakai et al applied the novel DL-based explainable graph chart diagram representation, which supports fetal cardiac ultrasound screening, which generally possesses a low rate of detection of congenital heart disease in their second-trimester stages because of difficulty in mastering the technique [20].…”

Section: Significance Of Ai In the Second And Third Trimestersmentioning

confidence: 99%

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

Malani¹,

Shrivastava²,

Raka³

2023

Cureus

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The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.

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Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening

Cited by 30 publications

References 74 publications

Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases

Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

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