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18 pages, 3824 KiB

Open AccessArticle

Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN

by Ning Yue, Jingwei Zhang, Jing Zhao, Qinyan Zhang, Xinshan Lin and Jijiang Yang

Bioengineering 2022, 9(8), 359; https://doi.org/10.3390/bioengineering9080359 - 1 Aug 2022

Cited by 2 | Viewed by 2573

Abstract

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), [...] Read more.

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient’s lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient’s bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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20 pages, 6317 KiB

Open AccessArticle

Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization

by Lu Gao, Zhiyu Chen, Lin Zang, Zhipeng Sun, Qing Wang and Guoxia Yu

Bioengineering 2022, 9(7), 316; https://doi.org/10.3390/bioengineering9070316 - 14 Jul 2022

Cited by 8 | Viewed by 2563

Abstract

Background: Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status. Methods: We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 participants younger than 24 years [...] Read more.

Background: Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status. Methods: We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 participants younger than 24 years old. The midpalatal suture region of interest (ROI) labeling was completed by two experienced clinical experts. The CBCT image fusion algorithm and image texture feature analysis algorithm were constructed and optimized. The age range prediction convolutional neural network (CNN) was conducted and tested. Results: The midpalatal suture fusion images contain complete semantic information for appraising midpalatal suture maturation and ossification status during the fast growth and development period. Correlation and homogeneity are the two texture features with the strongest relevance to chronological age. The overall performance of the age range prediction CNN model is satisfactory, especially in the 4 to 10 years range and the 17 to 23 years range, while for the 13 to 14 years range, the model performance is compromised. Conclusions: The image fusion algorithm can help show the overall perspective of the midpalatal suture in one fused image effectively. Furthermore, clinical decisions for maxillary transverse deficiency should be appraised by midpalatal suture image features directly rather than by age, especially in the 13 to 14 years range. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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13 pages, 1609 KiB

Open AccessArticle

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

by Gopichandh Danala, Sai Kiran Maryada, Warid Islam, Rowzat Faiz, Meredith Jones, Yuchen Qiu and Bin Zheng

Bioengineering 2022, 9(6), 256; https://doi.org/10.3390/bioengineering9060256 - 15 Jun 2022

Cited by 20 | Viewed by 2872

Abstract

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing [...] Read more.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3000 digital mammograms was assembled in which 1496 images depicted malignant lesions and 1504 images depicted benign lesions. Two CAD schemes were developed to classify breast lesions. The first scheme was developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme was built based on a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes were trained and tested using a 10-fold cross-validation method. Several score fusion methods were also investigated to classify breast lesions. CAD performances were evaluated and compared by the areas under the ROC curve (AUC). Results: The ResNet50 model-based CAD scheme yielded AUC = 0.85 ± 0.02, which was significantly higher than the radiomics feature-based CAD scheme with AUC = 0.77 ± 0.02 (p < 0.01). Additionally, the fusion of classification scores generated by the two CAD schemes did not further improve classification performance. Conclusion: This study demonstrates that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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11 pages, 1221 KiB

Open AccessArticle

Quality Management of Pulmonary Nodule Radiology Reports Based on Natural Language Processing

by Xiaolu Fei, Pengyu Chen, Lan Wei, Yue Huang, Yi Xin and Jia Li

Bioengineering 2022, 9(6), 244; https://doi.org/10.3390/bioengineering9060244 - 1 Jun 2022

Cited by 6 | Viewed by 2495

Abstract

To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this [...] Read more.

To investigate the feasibility of automated follow-up recommendations based on findings in radiology reports, this paper proposed a Natural Language Processing model specific for Pulmonary Nodule Radiology Reports. Unstructured findings used to describe pulmonary nodules in 48,091 radiology reports were processed in this study. We established an NLP model to extract information entities from findings of radiology reports, using deep learning and conditional random-field algorithms. Subsequently, we constructed a knowledge graph comprising 168 entities and four relationships, based on the export recommendations of the internationally renowned Fleischner Society for pulmonary nodules. These were employed in combination with rule templates to automatically generate follow-up recommendations. The automatically generated recommendations were then compared to the impression part of the reports to evaluate the matching rate of proper follow ups in the current situation. The NLP model identified eight types of entities with a recognition accuracy of up to 94.22%. A total of 43,898 out of 48,091 clinical reports were judged to contain appropriate follow-up recommendations, corresponding to the matching rate of 91.28%. The results show that NLP can be used on Chinese radiology reports to extract structured information at the content level, thereby realizing the prompt and intelligent follow-up suggestion generation or post-quality management of follow-up recommendations. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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16 pages, 4684 KiB

Open AccessArticle

EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels

by Yuqi Wang, Lijun Zhang, Pan Xia, Peng Wang, Xianxiang Chen, Lidong Du, Zhen Fang and Mingyan Du

Bioengineering 2022, 9(6), 231; https://doi.org/10.3390/bioengineering9060231 - 26 May 2022

Cited by 19 | Viewed by 4631

Abstract

Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been [...] Read more.

Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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20 pages, 2210 KiB

Open AccessArticle

Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset

by Adeyinka P. Adedigba, Steve A. Adeshina and Abiodun M. Aibinu

Bioengineering 2022, 9(4), 161; https://doi.org/10.3390/bioengineering9040161 - 6 Apr 2022

Cited by 24 | Viewed by 3744

Abstract

Cancer is the second leading cause of death globally, and breast cancer (BC) is the second most reported cancer. Although the incidence rate is reducing in developed countries, the reverse is the case in low- and middle-income countries. Early detection has been found [...] Read more.

Cancer is the second leading cause of death globally, and breast cancer (BC) is the second most reported cancer. Although the incidence rate is reducing in developed countries, the reverse is the case in low- and middle-income countries. Early detection has been found to contain cancer growth, prevent metastasis, ease treatment, and reduce mortality by 25%. The digital mammogram is one of the most common, cheapest, and most effective BC screening techniques capable of early detection of up to 90% BC incidence. However, the mammogram is one of the most difficult medical images to analyze. In this paper, we present a method of training a deep learning model for BC diagnosis. We developed a discriminative fine-tuning method which dynamically assigns different learning rates to each layer of the deep CNN. In addition, the model was trained using mixed-precision training to ease the computational demand of training deep learning models. Lastly, we present data augmentation methods for mammograms. The discriminative fine-tuning algorithm enables rapid convergence of the model loss; hence, the models were trained to attain their best performance within 50 epochs. Comparing the results, DenseNet achieved the highest accuracy of 0.998, while AlexNet obtained 0.988. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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20 pages, 4341 KiB

Open AccessArticle

Prediction of Pulmonary Function Parameters Based on a Combination Algorithm

by Ruishi Zhou, Peng Wang, Yueqi Li, Xiuying Mou, Zhan Zhao, Xianxiang Chen, Lidong Du, Ting Yang, Qingyuan Zhan and Zhen Fang

Bioengineering 2022, 9(4), 136; https://doi.org/10.3390/bioengineering9040136 - 25 Mar 2022

Cited by 4 | Viewed by 2500

Abstract

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function [...] Read more.

Objective: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. Methods: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. Results: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R² was found to be greater than 0.85 through a ten-fold cross-validation experiment. Conclusion: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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11 pages, 2043 KiB

Open AccessArticle

Geometric Parameter Identification of Medical Robot Based on Improved Beetle Antennae Search Algorithm

by Bin Kou, Dongcheng Ren and Shijie Guo

Bioengineering 2022, 9(2), 58; https://doi.org/10.3390/bioengineering9020058 - 29 Jan 2022

Cited by 11 | Viewed by 3046

Abstract

To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add [...] Read more.

To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add the random wandering behavior of the wolf colony algorithm to the search process of the beetle antennae search algorithm to strengthen its capability for local search. Following this, we improve the global convergence ability of the beetle antennae search algorithm by using the simulated annealing algorithm. We compare the accuracy of end positioning of the proposed algorithm with the frog-jumping algorithm and the beetle antennae search algorithm with variable step length through simulations. The results show that the proposed algorithm has a higher accuracy of convergence, and can significantly improve the accuracy of end positioning of the medical robot. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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Review

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16 pages, 626 KiB

Open AccessReview

Review on Facial-Recognition-Based Applications in Disease Diagnosis

by Jiaqi Qiang, Danning Wu, Hanze Du, Huijuan Zhu, Shi Chen and Hui Pan

Bioengineering 2022, 9(7), 273; https://doi.org/10.3390/bioengineering9070273 - 23 Jun 2022

Cited by 26 | Viewed by 8650

Abstract

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) [...] Read more.

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration. Full article

(This article belongs to the Special Issue Artificial Intelligence Based Computer-Aided Diagnosis)

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