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Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition
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Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition

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 (31 July 2024) | Viewed by 9162

Special Issue Editors

Prof. Dr. Luca Saba

E-Mail Website
Guest Editor
Department of Radiology, University of Cagliari, 09042 Cagliari, Italy
Interests: neuroradiology; vascular imaging; cardiovascular imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Jasjit S. Suri

E-Mail Website
Guest Editor
1. Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA 95661, USA
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), including deep learning and machine learning, is currently undergoing rapid development, having garnered substantial public attention in recent years. This Special Issue plans to focus on topics and issues regarding the development AI to become more meaningfully intelligent for lesion detection and analysis, scientific validations of AI systems, clinical evaluations of AI systems, bias detection in AI systems, high-speed AI systems, and edge-devices for AI systems, all these facets of AI enveloping different branches of medicine and leading to personalized and precision medicine.

Prof. Dr. Luca Saba
Dr. Jasjit S. Suri
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
  • deep learning
  • machine learning
  • lesion detection and analysis
  • diagnosis

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Related Special Issue

Published Papers (5 papers)

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Research

14 pages, 5903 KiB  
Article
Diagnostic Performance of Artificial Intelligence in Chest Radiographs Referred from the Emergency Department
by Julia López Alcolea, Ana Fernández Alfonso, Raquel Cano Alonso, Ana Álvarez Vázquez, Alejandro Díaz Moreno, David García Castellanos, Lucía Sanabria Greciano, Chawar Hayoun, Manuel Recio Rodríguez, Cristina Andreu Vázquez, Israel John Thuissard Vasallo and Vicente Martínez de Vega
Diagnostics 2024, 14(22), 2592; https://doi.org/10.3390/diagnostics14222592 - 18 Nov 2024
Viewed by 332
Abstract
Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of [...] Read more.
Background: The increasing integration of AI in chest X-ray evaluation holds promise for enhancing diagnostic accuracy and optimizing clinical workflows. However, understanding its performance in real-world clinical settings is essential. Objectives: In this study, we evaluated the sensitivity (Se) and specificity (Sp) of an AI-based software (Arterys MICA v29.4.0) alongside a radiology resident in interpreting chest X-rays referred from the emergency department (ED), using a senior radiologist’s assessment as the gold standard (GS). We assessed the concordance between the AI system and the resident, noted the frequency of doubtful cases for each category, identified how many were considered positive by the GS, and assessed variables that AI was not trained to detect. Methods: We conducted a retrospective observational study analyzing chest X-rays from a sample of 784 patients referred from the ED at our hospital. The AI system was trained to detect five categorical variables—pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture—and assign each a confidence label (“positive”, “doubtful”, or “negative”). Results: Sensitivity in detecting fractures and pneumothorax was high (100%) for both AI and the resident, moderate for pulmonary opacity (AI = 76%, resident = 71%), and acceptable for pleural effusion (AI = 60%, resident = 67%), with negative predictive values (NPV) above 95% and areas under the curve (AUC) exceeding 0.8. The resident showed moderate sensitivity (75%) for pulmonary nodules, while AI’s sensitivity was low (33%). AI assigned a “doubtful” label to some diagnoses, most of which were deemed negative by the GS; the resident expressed doubt less frequently. The Kappa coefficient between the resident and AI was fair (0.3) across most categories, except for pleural effusion, where concordance was moderate (0.5). Our study highlighted additional findings not detected by AI, including 16% prevalence of mediastinal abnormalities, 20% surgical materials, and 20% other pulmonary findings. Conclusions: Although AI demonstrated utility in identifying most primary findings—except for pulmonary nodules—its high NPV suggests it may be valuable for screening. Further training of the AI software and broadening its scope to identify additional findings could enhance its detection capabilities and increase its applicability in clinical practice. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition)
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24 pages, 3684 KiB  
Article
Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis
by Ana-Maria Trofin, Călin Gh. Buzea, Răzvan Buga, Maricel Agop, Lăcrămioara Ochiuz, Dragos Teodor Iancu and Lucian Eva
Diagnostics 2024, 14(12), 1268; https://doi.org/10.3390/diagnostics14121268 - 15 Jun 2024
Cited by 1 | Viewed by 706
Abstract
This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before [...] Read more.
This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC, and other metrics derived from confusion matrices. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.97, effectively complementing the broader model analysis. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like “control at one year”, “age of the patient”, and “beam-on time for volume V1 treated” were consistently influential across various models, albeit their impacts were interpreted differently depending on the model’s underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition)
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15 pages, 817 KiB  
Article
Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques
by Md. Monirul Islam, K. M. Rafiqul Alam, Jia Uddin, Imran Ashraf and Md Abdus Samad
Diagnostics 2023, 13(21), 3360; https://doi.org/10.3390/diagnostics13213360 - 1 Nov 2023
Cited by 4 | Viewed by 2947
Abstract
Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep [...] Read more.
Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition)
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32 pages, 11880 KiB  
Article
DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images
by Prabhav Sanga, Jaskaran Singh, Arun Kumar Dubey, Narendra N. Khanna, John R. Laird, Gavino Faa, Inder M. Singh, Georgios Tsoulfas, Mannudeep K. Kalra, Jagjit S. Teji, Mustafa Al-Maini, Vijay Rathore, Vikas Agarwal, Puneet Ahluwalia, Mostafa M. Fouda, Luca Saba and Jasjit S. Suri
Diagnostics 2023, 13(19), 3159; https://doi.org/10.3390/diagnostics13193159 - 9 Oct 2023
Cited by 6 | Viewed by 2075
Abstract
Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, [...] Read more.
Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models’ performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition)
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13 pages, 7837 KiB  
Article
The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy
by Ilona Vilkoite, Ivars Tolmanis, Hosams Abu Meri, Inese Polaka, Linda Mezmale, Linda Anarkulova, Marcis Leja and Aivars Lejnieks
Diagnostics 2023, 13(4), 701; https://doi.org/10.3390/diagnostics13040701 - 13 Feb 2023
Cited by 7 | Viewed by 2245
Abstract
Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to [...] Read more.
Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. Methods: A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). Results: None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. Conclusions: Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence—2nd Edition)
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