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Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning,...
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Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning, Computer-Aided Diagnosis/Detection, and Radiomics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 29744

Special Issue Editors

Dr. Giorgio De Nunzio

E-Mail Website
Guest Editor
Department of Mathematics and Physics, University of Salento, and DReAM (Laboratorio Diffuso di Ricerca Interdisciplinare Applicata alla Medicina), 73100 Lecce, Italy
Interests: physics applied to medicine; radiomics; computer-assisted detection/diagnosis; machine/deep learning; artificial neural networks; artificial intelligence; omics sciences; precision medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Luana Conte

E-Mail Website
Guest Editor
1. Department of Mathematics and Physics, University of Salento, Lecce, Italy
2. DReAM (Laboratorio Diffuso di Ricerca Interdisciplinare Applicata alla Medicina), 73100 Lecce, Italy
Interests: artificial intelligence in medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clinical decision support systems (CDSSs) are software tools designed to impact clinician’s decisions on patients’ care, during either the diagnostic procedure or treatment planning. Modern CDSSs may be based on artificial intelligence (e.g., expert systems, machine/deep learning applications, pattern recognition, image understanding). They may involve the automatic (and massive) extraction of measurable features from diagnostic images (the so-called radiomics approach) and possibly their association with the patient genetic profile (radiogenomics). Other sources of diagnostic data can be ECG or EEG time series or hematologic and blood chemistry tests. The calculated features are usually integrated into multidisciplinary predictive models where classification/regression/inference software systems allow information to be deduced for the management of diagnosis, treatment, and prognosis.  

This Special Issue focuses on all the practical applications of artificial intelligence in CDSSs. Besides papers describing models and algorithms working in all medical imaging modalities (e.g., X-ray, CT, MRI, ultrasound, nuclear medicine, molecular imaging, macroscopic and microscopic imaging, and multi-modality technologies) and other medical data, articles discussing human–computer interaction, workstation design, database management, and performance evaluation are also welcome.

Prof. Giorgio De Nunzio
Dr. Luana Conte
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. Applied Sciences 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 2400 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
  • AI
  • machine learning
  • deep learning
  • pattern recognition
  • radiomics
  • radiogenomics
  • neural networks
  • computer-assisted diagnosis
  • computer-assisted detection
  • computer-aided diagnosis
  • computer-aided detection, expert systems
  • medical informatics
  • big data
  • artificial neural networks
  • medical image analysis
  • segmentation

Published Papers (5 papers)

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Research

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22 pages, 3458 KiB  
Article
An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency Virus
by Luana I. C. C. Pinheiro, Maria Lúcia D. Pereira, Evandro C. de Andrade, Luciano C. Nunes, Wilson C. de Abreu, Pedro Gabriel Calíope D. Pinheiro, Raimir Holanda Filho and Plácido Rogerio Pinheiro
Appl. Sci. 2021, 11(21), 10457; https://doi.org/10.3390/app112110457 - 07 Nov 2021
Cited by 3 | Viewed by 2042
Abstract
Hybrid models to detect dementia based on Machine Learning can provide accurate diagnoses in individuals with neurological disorders and cognitive complications caused by Human Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine Learning algorithms associated with the multicriteria method [...] Read more.
Hybrid models to detect dementia based on Machine Learning can provide accurate diagnoses in individuals with neurological disorders and cognitive complications caused by Human Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine Learning algorithms associated with the multicriteria method of Verbal Decision Analysis (VDA). Dementia, which affects many HIV-infected individuals, refers to neurodevelopmental and mental disorders. Some manuals standardize the information used in the correct detection of neurological disorders with cognitive complications. Among the most common manuals used are the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th edition) of the American Psychiatric Association and the International Classification of Diseases, 10th edition (ICD-10)—both published by World Health Organization (WHO). The model is designed to explore the predictive of specific data. Furthermore, a well-defined database data set improves and optimizes the diagnostic models sought in the research. Full article
(This article belongs to the Special Issue Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning, Computer-Aided Diagnosis/Detection, and Radiomics)
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19 pages, 856 KiB  
Article
Early-, Late-, and Very Late-Term Prediction of Target Lesion Failure in Coronary Artery Stent Patients: An International Multi-Site Study
by Elisabeth Pachl, Alireza Zamanian, Myriam Stieler, Calvin Bahr and Narges Ahmidi
Appl. Sci. 2021, 11(15), 6986; https://doi.org/10.3390/app11156986 - 29 Jul 2021
Cited by 2 | Viewed by 1713
Abstract
The main intervention for coronary artery disease is stent implantation. We aim to predict post-intervention target lesion failure (TLF) months before its onset, an extremely challenging task in clinics. This post-intervention decision support tool helps physicians to identify at-risk patients much earlier and [...] Read more.
The main intervention for coronary artery disease is stent implantation. We aim to predict post-intervention target lesion failure (TLF) months before its onset, an extremely challenging task in clinics. This post-intervention decision support tool helps physicians to identify at-risk patients much earlier and to inform their follow-up care. We developed a novel machine-learning model with three components: a TLF predictor at discharge via a combination of nine conventional models and a super-learner, a risk score predictor for time-to-TLF, and an update function to manage the size of the at-risk cohort. We collected data in a prospective study from 120 medical centers in over 25 countries. All 1975 patients were enrolled during Phase I (2016–2020) and were followed up for five years post-intervention. During Phase I, 151 patients (7.6%) developed TLF, which we used for training. Additionally, 12 patients developed TLF after Phase I (right-censored). Our algorithm successfully classifies 1635 patients as not at risk (TNR = 90.23%) and predicts TLF for 86 patients (TPR = 52.76%), outperforming its training by identifying 33% of the right-censored patients. We also compare our model against five state of the art models, outperforming them all. Our prediction tool is able to optimize for both achieving higher sensitivity and maintaining a reasonable size for the at-risk cohort over time. Full article
(This article belongs to the Special Issue Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning, Computer-Aided Diagnosis/Detection, and Radiomics)
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16 pages, 624 KiB  
Article
A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study
by Marco Alì, Natascha Claudia D’Amico, Matteo Interlenghi, Marina Maniglio, Deborah Fazzini, Simone Schiaffino, Christian Salvatore, Isabella Castiglioni and Sergio Papa
Appl. Sci. 2021, 11(6), 2503; https://doi.org/10.3390/app11062503 - 11 Mar 2021
Cited by 2 | Viewed by 2291
Abstract
Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis [...] Read more.
Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%. Full article
(This article belongs to the Special Issue Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning, Computer-Aided Diagnosis/Detection, and Radiomics)
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15 pages, 1077 KiB  
Article
Intelligent Neonatal Sepsis Early Diagnosis System for Very Low Birth Weight Infants
by Fabio Tarricone, Antonio Brunetti, Domenico Buongiorno, Nicola Altini, Vitoantonio Bevilacqua, Antonio Del Vecchio and Flavia Petrillo
Appl. Sci. 2021, 11(1), 404; https://doi.org/10.3390/app11010404 - 04 Jan 2021
Cited by 3 | Viewed by 2834
Abstract
Neonatal sepsis is a critical pathology that particularly affects the neonates in intensive care, especially if they are preterm and low birth weight, with an incidence varying between 1and 40% according to the onset (early or late) of the disease. Prompt diagnostic and [...] Read more.
Neonatal sepsis is a critical pathology that particularly affects the neonates in intensive care, especially if they are preterm and low birth weight, with an incidence varying between 1and 40% according to the onset (early or late) of the disease. Prompt diagnostic and therapeutic interventions could reduce the high percentage of mortality that characterises this pathology, especially in the premature and low weight neonates. The HeRO score analyses the heart rate variability and represents the risk of contracting sepsis because of the hospitalization in the neonatal intensive care unit up to 24 h before the clinical signs. However, it has been demonstrated that the HeRO score can produce many false-positive cases, thus leading to the start of unnecessary antibiotic therapy. In this work, the authors propose an optimised artificial neural network model able to diagnose sepsis early based on the HeRO score along with a series of parameters strictly connected to the risk of neonatal sepsis. The proposed methodology shows promising results, outperforming the diagnostic accuracy of the only HeRO score and reducing the number of false positives, thus revealing itself to be a promising tool for supporting the clinicians in the daily clinical practice. Full article
(This article belongs to the Special Issue Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning, Computer-Aided Diagnosis/Detection, and Radiomics)
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Review

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23 pages, 317 KiB  
Review
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review
by Anna Markella Antoniadi, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A. Becker and Catherine Mooney
Appl. Sci. 2021, 11(11), 5088; https://doi.org/10.3390/app11115088 - 31 May 2021
Cited by 180 | Viewed by 19631
Abstract
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced [...] Read more.
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs. Full article
(This article belongs to the Special Issue Advances in Clinical Decision Support Systems: Artificial Intelligence, Machine/Deep Learning, Computer-Aided Diagnosis/Detection, and Radiomics)
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