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Artificial Intelligence in Lung Diseases 2.0

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 19777

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Prof. Dr. Cesar A. Moran

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Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: thoracic pathology (lung, pleura, and mediastinum)
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Dear Colleagues,

Precision medicine, more specifically artificial intelligence (AI) in the diagnosis and treatment of pulmonary diseases, has evolved in important ways. The developments in thoracic imaging, thoracic pathology, and thoracic oncology are meaningful components that play a role in bringing those modalities to the bedside. Digital radiology as well as digital pathology are becoming portable specialties that, combined with advanced oncological algorithms, can be used for the betterment of patients afflicted by different thoracic diseases. Such technological advancements should not be limited to neoplastic diseases but should also include other non-neoplastic processes in which such technology is applicable. In our current practice, the team approach to disease seems to have a better impact on the clinical outcomes of patients; therefore, it is highly important that these technologies, as they advance, become part of the armamentaria of tools that all clinicians need to be familiar with, and possibly by having this team approach, the different aspects of our individual specialties will also expand.

Diagnostics is dedicating one Special Issue to the role of AI in pulmonary diseases, knowing of the technological advancement that is taking place. The goal is to bring such advancements to all individuals involved in the care of patients afflicted by the gamut of pulmonary diseases. It is our hope that you will contribute to this Special Issue whether in pathology, radiology, oncology, or pulmonary medicine.

Prof. Dr. Cesar A. Moran
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Related Special Issue

Artificial Intelligence in Lung Diseases: 3rd Edition in Diagnostics

Published Papers (11 papers)

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Research

15 pages, 2604 KiB

Open AccessArticle

A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases

by Fevzi Yasin Kababulut, Damla Gürkan Kuntalp, Okan Düzyel, Nermin Özcan and Mehmet Kuntalp

Diagnostics 2023, 13(23), 3558; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13233558 - 28 Nov 2023

Viewed by 689

Abstract

The aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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13 pages, 3083 KiB

Open AccessArticle

Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images

by Dehua Hu, Xiang Li, Chao Lin, Yonggang Wu and Hao Jiang

Diagnostics 2023, 13(19), 3107; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13193107 - 30 Sep 2023

Cited by 1 | Viewed by 942

Abstract

(1) Background: Cell proliferation (Ki-67) has important clinical value in the treatment and prognosis of non-small cell lung cancer (NSCLC). However, current detection methods for Ki-67 are invasive and can lead to incorrect results. This study aimed to explore a deep learning classification model for the prediction of Ki-67 and the prognosis of NSCLC based on FDG-PET/CT images. (2) Methods: The FDG-PET/CT scan results of 159 patients with NSCLC confirmed via pathology were analyzed retrospectively, and the prediction models for the Ki-67 expression level based on PET images, CT images and PET/CT combined images were constructed using Densenet201. Based on a Ki-67 high expression score (HES) obtained from the prediction model, the survival rate of patients with NSCLC was analyzed using Kaplan–Meier and univariate Cox regression. (3) Results: The statistical analysis showed that Ki-67 expression was significantly correlated with clinical features of NSCLC, including age, gender, differentiation state and histopathological type. After a comparison of the three models (i.e., the PET model, the CT model, and the FDG-PET/CT combined model), the combined model was found to have the greatest advantage in Ki-67 prediction in terms of AUC (0.891), accuracy (0.822), precision (0.776) and specificity (0.902). Meanwhile, our results indicated that HES was a risk factor for prognosis and could be used for the survival prediction of NSCLC patients. (4) Conclusions: The deep-learning-based FDG-PET/CT radiomics classifier provided a novel non-invasive strategy with which to evaluate the malignancy and prognosis of NSCLC. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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12 pages, 3016 KiB

Open AccessArticle

A Convolutional Neural Network Architecture for Segmentation of Lung Diseases Using Chest X-ray Images

by Adel Sulaiman, Vatsala Anand, Sheifali Gupta, Yousef Asiri, M. A. Elmagzoub, Mana Saleh Al Reshan and Asadullah Shaikh

Diagnostics 2023, 13(9), 1651; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13091651 - 08 May 2023

Cited by 8 | Viewed by 1594

Abstract

The segmentation of lungs from medical images is a critical step in the diagnosis and treatment of lung diseases. Deep learning techniques have shown great promise in automating this task, eliminating the need for manual annotation by radiologists. In this research, a convolution neural network architecture is proposed for lung segmentation using chest X-ray images. In the proposed model, concatenate block is embedded to learn a series of filters or features used to extract meaningful information from the image. Moreover, a transpose layer is employed in the concatenate block to improve the spatial resolution of feature maps generated by a prior convolutional layer. The proposed model is trained using k-fold validation as it is a powerful and flexible tool for evaluating the performance of deep learning models. The proposed model is evaluated on five different subsets of the data by taking the value of k as 5 to obtain the optimized model to obtain more accurate results. The performance of the proposed model is analyzed for different hyper-parameters such as the batch size as 32, optimizer as Adam and 40 epochs. The dataset used for the segmentation of disease is taken from the Kaggle repository. The various performance parameters such as accuracy, IoU, and dice coefficient are calculated, and the values obtained are 0.97, 0.93, and 0.96, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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13 pages, 1935 KiB

Open AccessArticle

AI-Based CXR First Reading: Current Limitations to Ensure Practical Value

by Yuriy Vasilev, Anton Vladzymyrskyy, Olga Omelyanskaya, Ivan Blokhin, Yury Kirpichev and Kirill Arzamasov

Diagnostics 2023, 13(8), 1430; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13081430 - 16 Apr 2023

Cited by 2 | Viewed by 1611

Abstract

We performed a multicenter external evaluation of the practical and clinical efficacy of a commercial AI algorithm for chest X-ray (CXR) analysis (Lunit INSIGHT CXR). A retrospective evaluation was performed with a multi-reader study. For a prospective evaluation, the AI model was run on CXR studies; the results were compared to the reports of 226 radiologists. In the multi-reader study, the area under the curve (AUC), sensitivity, and specificity of the AI were 0.94 (CI95%: 0.87–1.0), 0.9 (CI95%: 0.79–1.0), and 0.89 (CI95%: 0.79–0.98); the AUC, sensitivity, and specificity of the radiologists were 0.97 (CI95%: 0.94–1.0), 0.9 (CI95%: 0.79–1.0), and 0.95 (CI95%: 0.89–1.0). In most regions of the ROC curve, the AI performed a little worse or at the same level as an average human reader. The McNemar test showed no statistically significant differences between AI and radiologists. In the prospective study with 4752 cases, the AUC, sensitivity, and specificity of the AI were 0.84 (CI95%: 0.82–0.86), 0.77 (CI95%: 0.73–0.80), and 0.81 (CI95%: 0.80–0.82). Lower accuracy values obtained during the prospective validation were mainly associated with false-positive findings considered by experts to be clinically insignificant and the false-negative omission of human-reported “opacity”, “nodule”, and calcification. In a large-scale prospective validation of the commercial AI algorithm in clinical practice, lower sensitivity and specificity values were obtained compared to the prior retrospective evaluation of the data of the same population. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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10 pages, 2598 KiB

Open AccessArticle

Prospective Evaluation of Quantitative F-18-FDG-PET/CT for Pre-Operative Thoracic Lymph Node Staging in Patients with Lung Cancer as a Target for Computer-Aided Diagnosis

by Philipp Genseke, Christoph Ferdinand Wielenberg, Jens Schreiber, Eva Luecke, Steffen Frese, Thorsten Walles and Michael Christoph Kreissl

Diagnostics 2023, 13(7), 1263; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13071263 - 27 Mar 2023

Viewed by 1259

Abstract

Purpose: Pre-operative assessment of thoracic lymphonodal (LN) involvement in patients with lung cancer (LC) is crucial when choosing the treatment modality. Visual assessment of F-18-FDG-PET/CT (PET/CT) is well established, however, there is still a need for prospective quantitative data to differentiate benign from malignant lesions which would simplify staging and guide the further implementation of computer-aided diagnosis (CAD). Methods: In this prospective study, 37 patients with confirmed lung cancer (m/f = 24/13; age: 70 [52–83] years) were analyzed. All patients underwent PET/CT and quantitative data (standardized uptake values) were obtained. Histological results were available for 101 thoracic lymph nodes. Quantitative data were matched to determine cut-off values for delineation between benign vs. malignant lymph nodes. Furthermore, a scoring system derived from these cut-off values was established. Statistical analyses were performed through ROC analysis. Results: Quantitative analysis revealed the optimal cut-off values (p < 0.01) for the differentiation between benign and malignant thoracic lymph nodes in patients suffering from lung cancer. The respective areas under the curve (AUC) ranged from 0.86 to 0.94. The highest AUC for a ratio of lymph node to healthy lung tissue was 0.94. The resulting accuracy ranged from 78.2% to 89.1%. A dedicated scoring system led to an AUC of 0.93 with a negative predictive value of 95.4%. Conclusion: Quantitative analysis of F-18-FDG-PET/CT data provides reliable results for delineation between benign and malignant thoracic lymph nodes. Thus, quantitative parameters can improve diagnostic accuracy and reliability and can also facilitate the handling of the steadily increasing number of clinical examinations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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11 pages, 1168 KiB

Open AccessArticle

Diagnostic Performance of Electromagnetic Navigation versus Virtual Navigation Bronchoscopy-Guided Biopsy for Pulmonary Lesions in a Single Institution: Potential Role of Artificial Intelligence for Navigation Planning

by Yuan-Ming Tsai, Yen-Shou Kuo, Kuan-Hsun Lin, Ying-Yi Chen and Tsai-Wang Huang

Diagnostics 2023, 13(6), 1124; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13061124 - 16 Mar 2023

Cited by 2 | Viewed by 2333

Abstract

Navigation bronchoscopy is an emerging technique used to evaluate pulmonary lesions. Using Veran’s SPiN electromagnetic navigation bronchoscopy (ENB) and Archimedes virtual bronchoscopy navigation (VBN), this study aimed to compare the accuracy and safety of these procedures for lung lesions and to identify potentially relevant knowledge for the application of artificial intelligence in interventional pulmonology in a single institute. This single-center, retrospective study compared the ENB and VBN results in patients with pulmonary lesions unsuitable for biopsy via percutaneous transthoracic needle biopsy methods. A total of 35 patients who underwent navigation bronchoscopy for pulmonary lesion diagnosis were enrolled. Nineteen patients were stratified in the ENB group, and sixteen were in the VBN group. The mean age of this cohort was 67.6 ± 9.9 years. The mean distance of the lesion from the pleural surface was 16.1 ± 11.7 mm (range: 1.0–41.0 mm), and most lesions were a solid pattern (n = 33, 94.4%). There were 32 cases (91.4%) of pulmonary lesions with an air-bronchus sign. A statistically significant difference was found between pulmonary size and transparenchymal nodule access (p = 0.049 and 0.037, respectively). The navigation success rate was significantly higher in the VBN group (93.8% vs. 78.9%). Moreover, no procedure-related complications or mortality were noted. The radiographic characteristics, such as size or solid component, can affect the selection of the biopsy procedure, either ENB or VBN. Navigation bronchoscopy-guided biopsy demonstrated acceptable accuracy and a good safety profile in evaluating pulmonary lesions when the percutaneous approach was challenging or life threatening. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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9 pages, 447 KiB

Open AccessArticle

Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov

by Bingjie Li, Lisha Jiang, Dan Lin and Jingsi Dong

Diagnostics 2022, 12(12), 3046; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12123046 - 05 Dec 2022

Viewed by 1206

Abstract

Clinical trials are the most effective tools to evaluate the advantages of various diagnostic and treatment modalities. AI used in medical issues, including screening, diagnosis, and treatment decisions, improves health outcomes and patient experiences. This study’s objective was to investigate the traits of registered trials on artificial intelligence for lung disease. Clinical studies on AI for lung disease that were present in the ClinicalTrials.gov database were searched, and fifty-three registered trials were included. Forty-six (72.1%) were observational trials, compared to seven (27.9%) that were interventional trials. Only eight trials (15.4%) were completed. Thirty (56.6%) trials were accepting applicants. Clinical studies often included a large number of cases; for example, 24 (32.0%) trials included samples of 100–1000 cases, while 14 (17.5%) trials included samples of 1000–2000 cases. Of the interventional trials, twenty (15.7%) were retrospective studies and twenty (65.7%) were prospective studies. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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15 pages, 3435 KiB

Open AccessArticle

Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings

by Parisa Kaviani, Mannudeep K. Kalra, Subba R. Digumarthy, Reya V. Gupta, Giridhar Dasegowda, Ammar Jagirdar, Salil Gupta, Preetham Putha, Vidur Mahajan, Bhargava Reddy, Vasanth K. Venugopal, Manoj Tadepalli, Bernardo C. Bizzo and Keith J. Dreyer

Diagnostics 2022, 12(10), 2382; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12102382 - 30 Sep 2022

Cited by 4 | Viewed by 2796

Abstract

Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1—not important; 5—critical importance). All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Data were analyzed to obtain area under the ROC curve (AUC). Results: Of 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (n = 157), consolidation (60), linear opacities (37), mediastinal widening (21), hilar enlargement (17), pleural effusions (11), rib fractures (6) and pneumothoraces (3). AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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13 pages, 2188 KiB

Open AccessArticle

Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study

by Cheng-Chun Yang, Chin-Yu Chen, Yu-Ting Kuo, Ching-Chung Ko, Wen-Jui Wu, Chia-Hao Liang, Chun-Ho Yun and Wei-Ming Huang

Diagnostics 2022, 12(4), 1002; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12041002 - 15 Apr 2022

Cited by 9 | Viewed by 2374

Abstract

Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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13 pages, 2186 KiB

Open AccessArticle

Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers

by Kuang-Ming Liao, Shian-Chin Ko, Chung-Feng Liu, Kuo-Chen Cheng, Chin-Ming Chen, Mei-I Sung, Shu-Chen Hsing and Chia-Jung Chen

Diagnostics 2022, 12(4), 975; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12040975 - 13 Apr 2022

Cited by 9 | Viewed by 2376

Abstract

Successful weaning from prolonged mechanical ventilation (MV) is an important issue in respiratory care centers (RCCs). Delayed or premature extubation increases both the risk of adverse outcomes and healthcare costs. However, the accurate evaluation of the timing of successful weaning from MV is very challenging in RCCs. This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best model in RCC settings. A total of 670 intubated patients in the RCC in Chi Mei Medical Center were included in the study. Twenty-six feature variables were selected to build the predictive models with artificial intelligence (AI)/machine-learning (ML) algorithms. An interactive dashboard with the best model was developed and deployed. A preliminary impact analysis was then conducted. Our results showed that all seven predictive models had a high area under the receiver operating characteristic curve (AUC), which ranged from 0.792 to 0.868. The preliminary impact analysis revealed that the mean number of ventilator days required for the successful weaning of the patients was reduced by 0.5 after AI intervention. The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. However, a systematic prospective study of AI intervention is still needed. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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11 pages, 5889 KiB

Open AccessArticle

A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study

by Lena Bundschuh, Vesna Prokic, Matthias Guckenberger, Stephanie Tanadini-Lang, Markus Essler and Ralph A. Bundschuh

Diagnostics 2022, 12(3), 576; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12030576 - 23 Feb 2022

Cited by 2 | Viewed by 1548

Abstract

Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied. Full article

(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases 2.0)

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