Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care

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 April 2024) | Viewed by 20698

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National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: machine learning; artificial intelligence; medical image analysis; image informatics; multimodal data analysis; data science; NCI (cervical cancer)
Special Issues, Collections and Topics in MDPI journals
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: cancers; machine learning; artificial intelligence; image processing; computer vision; biomedical informatics; data science
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: machine learning; artificial intelligence; computer vision; medical image analysis; data science; biomaterial-associated infections; music therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the realm of modern healthcare, the fusion of artificial intelligence (AI) with medical imaging has emerged as a cornerstone for revolutionizing screening, diagnostics, and clinical care across a spectrum of diseases. This evolution is particularly pivotal at a time when the global healthcare industry is striving to enhance accuracy, efficiency, and accessibility in patient care services.

The application of AI and machine learning (ML) in medical imaging extends beyond mere automation; it introduces a new era of precision medicine where diagnostics and treatment are highly tailored to individual patient profiles. Innovations in deep learning (DL) algorithms have shown exceptional promise in rapidly assessing complex imaging data, serving as an adjunct to expert clinical judgment, and significantly minimizing variability in diagnostic interpretations. These technological advancements have the potential not only to streamline workflow, but also to facilitate the early detection of conditions, thereby profoundly impacting treatment outcomes and patient prognosis.

However, the journey towards fully integrating AI into clinical settings is fraught with challenges. The effectiveness of these AI and ML methodologies heavily depends on the quality, volume, and annotation of training datasets. The scarcity of well-curated and annotated medical images, compounded by the inherent imbalance between cases and controls, poses a significant hurdle in model training and validation. Moreover, the presence of noisy and incomplete data in real-world medical datasets can lead to unreliable algorithm performance and potential biases.

The Special Issue entitled "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care" endeavors to illuminate the groundbreaking advancements ushered in by AI/ML across various medical imaging modalities. This edition seeks to explore innovative AI/ML methodologies tailored for image-based screening, diagnostics, and clinical management, overcoming the barriers of limited and imperfect medical data. We are particularly interested in original research and comprehensive reviews that present novel methods for training ML/DL models capable of navigating the complexities of real-world medical imaging data. Through this compilation, we aim to share insights into state-of-the-art AI applications that hold the promise of making substantial contributions to overcoming global health challenges.

We warmly invite contributions from researchers and practitioners pioneering in this field to submit their novel and unpublished work in this area.

Dr. Sameer Antani
Dr. Zhiyun Xue
Dr. Sivaramakrishnan Rajaraman
Guest Editors

Manuscript Submission Information

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • image-based screening and diagnostics
  • computer-aided diagnosis
  • machine learning and deep learning
  • approaches for learning noise invariant features
  • approaches to handling data imbalanced training scenarios
  • learning with noisy/corrupted data or uncertain labels
  • weakly supervised, semi-supervised, and self-supervised learning
  • learning in real-world and open-environment scenarios
  • cardiothoracic and pulmonary diseases
  • radiographic imaging
  • computed tomography (CT)
  • chest X-rays (CXRs)
  • echo ultrasound

Related Special Issue

Published Papers (10 papers)

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Research

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12 pages, 5828 KiB  
Article
Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs
by Yangqin Feng, Jordan Sim Zheng Ting, Xinxing Xu, Chew Bee Kun, Edward Ong Tien En, Hendra Irawan Tan Wee Jun, Yonghan Ting, Xiaofeng Lei, Wen-Xiang Chen, Yan Wang, Shaohua Li, Yingnan Cui, Zizhou Wang, Liangli Zhen, Yong Liu, Rick Siow Mong Goh and Cher Heng Tan
Diagnostics 2023, 13(8), 1397; https://doi.org/10.3390/diagnostics13081397 - 12 Apr 2023
Cited by 1 | Viewed by 1663
Abstract
Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network [...] Read more.
Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents’ diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents’ performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue. Full article
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14 pages, 3269 KiB  
Article
Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images
by Abdul Rahaman Wahab Sait and Ashit Kumar Dutta
Diagnostics 2023, 13(7), 1312; https://doi.org/10.3390/diagnostics13071312 - 31 Mar 2023
Cited by 4 | Viewed by 2192
Abstract
Coronary artery disease (CAD) is one of the major causes of fatalities across the globe. The recent developments in convolutional neural networks (CNN) allow researchers to detect CAD from computed tomography (CT) images. The CAD detection model assists physicians in identifying cardiac disease [...] Read more.
Coronary artery disease (CAD) is one of the major causes of fatalities across the globe. The recent developments in convolutional neural networks (CNN) allow researchers to detect CAD from computed tomography (CT) images. The CAD detection model assists physicians in identifying cardiac disease at earlier stages. The recent CAD detection models demand a high computational cost and a more significant number of images. Therefore, this study intends to develop a CNN-based CAD detection model. The researchers apply an image enhancement technique to improve the CT image quality. The authors employed You look only once (YOLO) V7 for extracting the features. Aquila optimization is used for optimizing the hyperparameters of the UNet++ model to predict CAD. The proposed feature extraction technique and hyperparameter tuning approach reduces the computational costs and improves the performance of the UNet++ model. Two datasets are utilized for evaluating the performance of the proposed CAD detection model. The experimental outcomes suggest that the proposed method achieves an accuracy, recall, precision, F1-score, Matthews correlation coefficient, and Kappa of 99.4, 98.5, 98.65, 98.6, 95.35, and 95 and 99.5, 98.95, 98.95, 98.95, 96.35, and 96.25 for datasets 1 and 2, respectively. In addition, the proposed model outperforms the recent techniques by obtaining the area under the receiver operating characteristic and precision-recall curve of 0.97 and 0.95, and 0.96 and 0.94 for datasets 1 and 2, respectively. Moreover, the proposed model obtained a better confidence interval and standard deviation of [98.64–98.72] and 0.0014, and [97.41–97.49] and 0.0019 for datasets 1 and 2, respectively. The study’s findings suggest that the proposed model can support physicians in identifying CAD with limited resources. Full article
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24 pages, 2978 KiB  
Article
Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System
by Dana Li, Lea Marie Pehrson, Rasmus Bonnevie, Marco Fraccaro, Jakob Thrane, Lea Tøttrup, Carsten Ammitzbøl Lauridsen, Sedrah Butt Balaganeshan, Jelena Jankovic, Tobias Thostrup Andersen, Alyas Mayar, Kristoffer Lindskov Hansen, Jonathan Frederik Carlsen, Sune Darkner and Michael Bachmann Nielsen
Diagnostics 2023, 13(6), 1070; https://doi.org/10.3390/diagnostics13061070 - 11 Mar 2023
Cited by 1 | Viewed by 1407
Abstract
A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray [...] Read more.
A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered “gold standard”. Matthew’s correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to “gold standard” (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology. Full article
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18 pages, 3417 KiB  
Article
Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection
by Zhiyun Xue, Feng Yang, Sivaramakrishnan Rajaraman, Ghada Zamzmi and Sameer Antani
Diagnostics 2023, 13(6), 1068; https://doi.org/10.3390/diagnostics13061068 - 11 Mar 2023
Cited by 4 | Viewed by 1545
Abstract
Domain shift is one of the key challenges affecting reliability in medical imaging-based machine learning predictions. It is of significant importance to investigate this issue to gain insights into its characteristics toward determining controllable parameters to minimize its impact. In this paper, we [...] Read more.
Domain shift is one of the key challenges affecting reliability in medical imaging-based machine learning predictions. It is of significant importance to investigate this issue to gain insights into its characteristics toward determining controllable parameters to minimize its impact. In this paper, we report our efforts on studying and analyzing domain shift in lung region detection in chest radiographs. We used five chest X-ray datasets, collected from different sources, which have manual markings of lung boundaries in order to conduct extensive experiments toward this goal. We compared the characteristics of these datasets from three aspects: information obtained from metadata or an image header, image appearance, and features extracted from a pretrained model. We carried out experiments to evaluate and compare model performances within each dataset and across datasets in four scenarios using different combinations of datasets. We proposed a new feature visualization method to provide explanations for the applied object detection network on the obtained quantitative results. We also examined chest X-ray modality-specific initialization, catastrophic forgetting, and model repeatability. We believe the observations and discussions presented in this work could help to shed some light on the importance of the analysis of training data for medical imaging machine learning research, and could provide valuable guidance for domain shift analysis. Full article
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18 pages, 3890 KiB  
Article
Assessing the Impact of Image Resolution on Deep Learning for TB Lesion Segmentation on Frontal Chest X-rays
by Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi, Zhiyun Xue and Sameer Antani
Diagnostics 2023, 13(4), 747; https://doi.org/10.3390/diagnostics13040747 - 16 Feb 2023
Cited by 3 | Viewed by 1635
Abstract
Deep learning (DL) models are state-of-the-art in segmenting anatomical and disease regions of interest (ROIs) in medical images. Particularly, a large number of DL-based techniques have been reported using chest X-rays (CXRs). However, these models are reportedly trained on reduced image resolutions for [...] Read more.
Deep learning (DL) models are state-of-the-art in segmenting anatomical and disease regions of interest (ROIs) in medical images. Particularly, a large number of DL-based techniques have been reported using chest X-rays (CXRs). However, these models are reportedly trained on reduced image resolutions for reasons related to the lack of computational resources. Literature is sparse in discussing the optimal image resolution to train these models for segmenting the tuberculosis (TB)-consistent lesions in CXRs. In this study, we investigated the performance variations with an Inception-V3 UNet model using various image resolutions with/without lung ROI cropping and aspect ratio adjustments and identified the optimal image resolution through extensive empirical evaluations to improve TB-consistent lesion segmentation performance. We used the Shenzhen CXR dataset for the study, which includes 326 normal patients and 336 TB patients. We proposed a combinatorial approach consisting of storing model snapshots, optimizing segmentation threshold and test-time augmentation (TTA), and averaging the snapshot predictions, to further improve performance with the optimal resolution. Our experimental results demonstrate that higher image resolutions are not always necessary; however, identifying the optimal image resolution is critical to achieving superior performance. Full article
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15 pages, 545 KiB  
Article
Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support
by Chadi Barakat, Marcel Aach, Andreas Schuppert, Sigurður Brynjólfsson, Sebastian Fritsch and Morris Riedel
Diagnostics 2023, 13(3), 391; https://doi.org/10.3390/diagnostics13030391 - 20 Jan 2023
Cited by 1 | Viewed by 1743
Abstract
The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, [...] Read more.
The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support. Full article
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15 pages, 2341 KiB  
Article
Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays—An Early Step in the Development of a Deep Learning-Based Decision Support System
by Dana Li, Lea Marie Pehrson, Lea Tøttrup, Marco Fraccaro, Rasmus Bonnevie, Jakob Thrane, Peter Jagd Sørensen, Alexander Rykkje, Tobias Thostrup Andersen, Henrik Steglich-Arnholm, Dorte Marianne Rohde Stærk, Lotte Borgwardt, Kristoffer Lindskov Hansen, Sune Darkner, Jonathan Frederik Carlsen and Michael Bachmann Nielsen
Diagnostics 2022, 12(12), 3112; https://doi.org/10.3390/diagnostics12123112 - 9 Dec 2022
Cited by 3 | Viewed by 1682
Abstract
Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency [...] Read more.
Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency among radiologists when using a novel diagnostic labeling scheme for chest X-rays. Six radiologists with experience ranging from one to sixteen years, annotated a set of 100 fully anonymized chest X-rays. The blinded radiologists annotated on two separate occasions. Statistical analyses were done using Randolph’s kappa and PABAK, and the proportions of specific agreements were calculated. Fair-to-excellent agreement was found for all labels among the annotators (Randolph’s Kappa, 0.40–0.99). The PABAK ranged from 0.12 to 1 for the two-reader inter-rater agreement and 0.26 to 1 for the intra-rater agreement. Descriptive and broad labels achieved the highest proportion of positive agreement in both the inter- and intra-reader analyses. Annotating findings with specific, interpretive labels were found to be difficult for less experienced radiologists. Annotating images with descriptive labels may increase agreement between radiologists with different experience levels compared to annotation with interpretive labels. Full article
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Review

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64 pages, 1229 KiB  
Review
Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years
by Elena Stamate, Alin-Ionut Piraianu, Oana Roxana Ciobotaru, Rodica Crassas, Oana Duca, Ana Fulga, Ionica Grigore, Vlad Vintila, Iuliu Fulga and Octavian Catalin Ciobotaru
Diagnostics 2024, 14(11), 1103; https://doi.org/10.3390/diagnostics14111103 - 26 May 2024
Viewed by 1413
Abstract
Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, [...] Read more.
Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. Full article
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16 pages, 928 KiB  
Review
Diagnostic AI and Cardiac Diseases
by Dilber Uzun Ozsahin, Cemre Ozgocmen, Ozlem Balcioglu, Ilker Ozsahin and Berna Uzun
Diagnostics 2022, 12(12), 2901; https://doi.org/10.3390/diagnostics12122901 - 22 Nov 2022
Cited by 3 | Viewed by 2094
Abstract
(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key [...] Read more.
(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017–2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors’ own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere. Full article
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Other

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12 pages, 587 KiB  
Systematic Review
Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review
by Alina Cornelia Pacurari, Sanket Bhattarai, Abdullah Muhammad, Claudiu Avram, Alexandru Ovidiu Mederle, Ovidiu Rosca, Felix Bratosin, Iulia Bogdan, Roxana Manuela Fericean, Marius Biris, Flavius Olaru, Catalin Dumitru, Gianina Tapalaga and Adelina Mavrea
Diagnostics 2023, 13(13), 2145; https://doi.org/10.3390/diagnostics13132145 - 22 Jun 2023
Cited by 4 | Viewed by 3313
Abstract
The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures [...] Read more.
The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case–control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice. Full article
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