Autodetect extracranial and intracranial artery stenosis by machine learning using ultrasound

Hsu, Kuang-Hung; Lin, Ching-Heng; Johnson, Kory R.; Liu, Chi‐Hung; Chang, Ting‐Yu; Huang, Kuo‐Lun; Fann, Yang-Cheng; Lee, Tsong‐Hai

doi:10.1016/j.compbiomed.2019.103569

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…The higher accuracy of the extra tree classifier model is attributed to the ensemble of trees where each tree is trained randomly. In particular, a random cut-point is available in the extra model instead of trying to find an optimal cut-point for each feature at each node, which leads to more different trees from each other when using this method and it is less likely to aggregate the errors and more likely to avoid overfitting. , To sum up, the extra tree ML model demonstrates decent accuracy to describe the aqueous stability of the solvent engineered halide perovskite films, which can then be employed to construct the virtual design space and conduct materials screening.…”

Section: Resultsmentioning

confidence: 99%

Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials

Huang,

Li,

Zhang

2023

ACS Appl. Mater. Interfaces

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Solvent treatment is critical to improving the stability of halide perovskite materials that suffer from notorious issues that inhibit their industrial deployment; however, the complicated perovskite virtual design space with different types of solvent modifiers is inaccessible to traditional trial-and-error methods. In this study, machine learning is employed to predict stable multiple solvent-modified perovskite films under hostile conditions, and a complicated quinary solvent system "DMSO + DMF + toluene + NMP + GBL" is effectively identified to significantly improve the optoelectronic stability of CH 3 NH 3 PbI 3 in water. The "combinatorial solvent design" approach is realized by an extra tree machine learning model, which leads to a prediction dataset containing aqueous stability labels of 6720 new quinary solvent/perovskite systems. Importantly, the accuracy of the machine learning model is verified via photoelectrochemical experiments, achieving an experimental accuracy of 80%. A machine learning-predicted quinary solvent system offers significantly enhanced aqueous stability and 1000 times larger aqueous photocurrents, compared with the control CH 3 NH 3 PbI 3 film under the same hostile conditions. This study demonstrates the efficacy of machine learning for solvent design toward stable halide perovskite materials under hostile conditions.

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Section: Resultsmentioning

confidence: 99%

Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials

Huang,

Li,

Zhang

2023

ACS Appl. Mater. Interfaces

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“… 11 , 12 The MLP models have been effectively applied to the diagnosis of liver cancer and breast cancer, to predict LNM status in breast cancer patients, and to assess the risk of cardiovascular disease in patients. 35 - 37 It is worth noting that the increase of hidden layers will also increase the complexity of the model. Studies have shown that an overly complex model can easily lead to overfitting and reduce the performance of the model for unfamiliar objects.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis

Jin¹,

Chen²,

Lin³

et al. 2023

Turk J Gastroenterol

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Background: Development of a radiomics model for predicting lymph node metastasis status in rectal cancer patients based on 3-dimensional endoanal rectal ultrasound images. Methods: This study retrospectively included 79 patients (41 with lymph node metastasis positive and 38 with lymph node metastasis negative) diagnosed with rectal cancer in our hospital from January 2018 to February 2022. The tumor’s region of interest is first delineated by radiologists, from which radiomics features are extracted. Radiomics features were then selected by independent samples t -test, correlation coefficient analysis between features, and least absolute shrinkage and regression with selection operator. Finally, a multilayer neural network model is developed using the selected radiomics features, and nested cross-validation is performed on it. These models were validated by assessing their diagnostic performance and comparing the areas under the curve and recall rate curve in the test set. Results: The areas under the curve of radiologist was 0.662 and the F1 score was 0.632. Thirty-four radiomics features were significantly associated with lymph node metastasis ( P < .05), and 10 features were finally selected for developing multilayer neural network models. The areas under the curve of the multilayer neural network models were 0.787, 0.761, 0.853, and the mean areas under the curve was 0.800. The F1 scores of the multilayer neural network models were 0.738, 0.740, and 0.818, and the mean F1 score was 0.771. Conclusions: Radiomics models based on 3-dimensional endoanal rectal ultrasound can be used to identify lymph node metastasis status in rectal cancer patient with good diagnostic performance.

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“…Among the six model versions, Self-ResAttentioNet18_Q1 had the highest classification accuracy at 96.05%, along with the highest recall (96.05%) and the highest specificity (96.09%). A comparative analysis of our proposed model with the existing literature [ 17 , 18 , 45 , 46 ] in the normal vs. abnormal classification using ICA or MCA waves has been done in this study to evaluate the performance of Self-AttentioNet18 against contemporary existing models. From that analysis, it can be concluded that the accuracy of the Self-AttentioNet18 in classifying healthy subjects and traumatic brain injured subjects is 6.88% greater than the existing state-of-the-art result [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

et al. 2023

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Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator’s skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.

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Autodetect extracranial and intracranial artery stenosis by machine learning using ultrasound

Cited by 13 publications

References 34 publications

Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials

Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

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