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Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 41592

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Special Issue Editors

Dr. Andrea Barucci

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Guest Editor

Institute of Applied Physics “Nello Carrara”, CNR-IFAC, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
Interests: machine learning; deep learning; radiomics; biophotonics
Special Issues, Collections and Topics in MDPI journals

Dr. Chiara Marzi

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Institute of applied physics "Nello Carrara" (IFAC), National Research Council (CNR), 50127 Florence, Italy
Interests: evaluation of brain complexity by fractal analysis from structural and functional MRI images; study of neurodegenerative and vascular diseases from diffusion-weighted images (DTI, Diffusion Tensor Imaging; DKI, Diffusion Kurtosis Imaging; NODDI, Neurite Orientation Dispersion and Density Imaging); development of machine learning techniques for neuroimaging data

Dr. Marco Giannelli

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Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126 Pisa, Italy
Interests: medical physics; magnetic resonance imaging; Gaussian and non-Gaussian diffusion-MRI; diffusion tensor imaging; diffusion kurtosis imaging; functional-MRI; voxel-based morphometry; quantitative MRI; radiomics; computed tomography imaging; quality controls in medical imaging

Special Issue Information

Dear Colleagues,

The field of artificial intelligence (AI) applications in healthcare and precision medicine is growing due to the power of this disruptive technology. Today, we have an extremely broad range of applications of these methods, ranging from clinical images classification and organ/lesion segmentation, to wearable devices for monitoring health status.

This Special Issue focuses on application of AI in diagnosis, rehabilitation and screening, aiming to demonstrate state-of-the-art works in employing these technologies for effective and efficient healthcare applications.

Although many papers and algorithms have been devoted to this topic, there is still so much we can do to move AI from benchmark to bedside.

We then encourage the submission of manuscripts for this forthcoming Special Issue on all aspects pertinent to the developments of artificial intelligence in healthcare for diagnosis, rehabilitation and screening.

Reviews and original research articles are welcome. Reviews should provide an up-to-date and critical overview of state-of-the-art technologies, while original research papers can describe the utilization of AI applications in healthcare, their critical aspects, limitations and challenges, or new concepts and fundamental studies.

If you have suggestions that you would like to discuss beforehand, please feel free to contact me. I look forward to and welcome your participation in this Special Issue.

Dr. Andrea Barucci
Dr. Chiara Marzi
Dr. Marco Giannelli
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

healthcare
radiomics
machine learning
deep learning
artificial intelligence
clinical imaging
medical physics
radiology
oncology
radiotherapy
diagnosis
rehabilitation
screening

Published Papers (13 papers)

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Research

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15 pages, 9137 KiB

Open AccessArticle

A Hybrid Artificial Intelligence Model for Detecting Keratoconus

by Zaid Abdi Alkareem Alyasseri, Ali H. Al-Timemy, Ammar Kamal Abasi, Alexandru Lavric, Husam Jasim Mohammed, Hidenori Takahashi, Jose Arthur Milhomens Filho, Mauro Campos, Rossen M. Hazarbassanov and Siamak Yousefi

Appl. Sci. 2022, 12(24), 12979; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412979 - 17 Dec 2022

Cited by 2 | Viewed by 1742

Abstract

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 and 579 KCN4) from Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo in Brazil and 1531 eyes (Healthy = 400, KCN1 = 378, KCN2 = 285, KCN3 = 200, KCN4 = 88) from Department of Ophthalmology, Jichi Medical University, Tochigi in Japan and used several accuracy metrics including Precision, Recall, F-Score, and Purity. We compared the proposed method with three other standard unsupervised algorithms including k-means, Kmedoids, and Spectral cluster. Based on two independent datasets, the proposed model outperformed the other algorithms, and thus could provide improved identification of the corneal status of the patients with keratoconus. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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13 pages, 2632 KiB

Open AccessArticle

LASSO-Cox Modeling of Survival Using High-Resolution CT-Based Radiomic Features in a Cohort of COVID-19 Patients and Its Generalizability to Standard Image Reconstruction

by Giulia Paolani, Lorenzo Spagnoli, Maria Francesca Morrone, Miriam Santoro, Francesca Coppola, Silvia Strolin, Rita Golfieri and Lidia Strigari

Appl. Sci. 2022, 12(23), 12065; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312065 - 25 Nov 2022

Viewed by 922

Abstract

Background: Few studies have focused on predicting the overall survival (OS) of patients affected by SARS-CoV-2 (i.e., COVID-19) using radiomic features (RFs) extracted from computer tomography (CT) images. Reconstruction of CT scans might potentially affect the values of RFs. Methods: Out of 435 patients, 239 had the scans reconstructed with a single modality, and hence, were used for training/testing, and 196 were reconstructed with two modalities were used as validation to evaluate RFs robustness to reconstruction. During training, the dataset was split into train/test using a 70/30 proportion, randomizing the procedure 100 times to obtain 100 different models. In all cases, RFs were normalized using the z-score and then given as input into a Cox proportional-hazards model regularized with the Least Absolute Shrinkage and Selection Operator (LASSO-Cox), used for feature selection and developing a robust model. The RFs retained multiple times in the models were also included in a final LASSO-Cox for developing the predictive model. Thus, we conducted sensitivity analysis increasing the number of retained RFs with an occurrence cut-off from 11% to 60%. The Bayesian information criterion (BIC) was used to identify the cut-off to build the optimal model. Results: The best BIC value indicated 45% as the optimal occurrence cut-off, resulting in five RFs used for generating the final LASSO-Cox. All the Kaplan-Meier curves of training and validation datasets were statistically significant in identifying patients with good and poor prognoses, irrespective of CT reconstruction. Conclusions: The final LASSO-Cox model maintained its predictive ability for predicting the OS in COVID-19 patients irrespective of CT reconstruction algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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12 pages, 492 KiB

Open AccessArticle

Artificial Intelligence-Assisted Diagnosis for Early Intervention Patients

by Ignacio Sierra, Norberto Díaz-Díaz, Carlos Barranco and Rocío Carrasco-Villalón

Appl. Sci. 2022, 12(18), 8953; https://0-doi-org.brum.beds.ac.uk/10.3390/app12188953 - 06 Sep 2022

Cited by 2 | Viewed by 1798

Abstract

The use of artificial intelligence to aid decision making is widely adopted today. Its application is found in different areas, among which the medical one is the most disruptive. However, there are few or no applications in Early Care that aid in the diagnosis and automatic assignment of therapy processes for children to help these centers. The objective of this work is to make a first approach to the problem and carry out a real proof of concept that demonstrates that this type of system can be useful in Early Care where the diagnosis and subsequent treatment must be determined by a multidisciplinary team. To measure the quality of the use of this type of technology, different machine learning techniques will be used on a real data set provided by the San Juan de Dios Hospital. This study will allow us to analyze the behavior of these techniques compared to traditional diagnosis. To make this comparison, there will be a qualified point of view in the field of children diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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16 pages, 3773 KiB

Open AccessArticle

Complete Blood Cell Detection and Counting Based on Deep Neural Networks

by Shin-Jye Lee, Pei-Yun Chen and Jeng-Wei Lin

Appl. Sci. 2022, 12(16), 8140; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168140 - 14 Aug 2022

Cited by 9 | Viewed by 4380

Abstract

Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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14 pages, 1531 KiB

Open AccessArticle

Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma

by Simona Marzi, Francesca Piludu, Ilaria Avanzolini, Valerio Muneroni, Giuseppe Sanguineti, Alessia Farneti, Pasqualina D’Urso, Maria Benevolo, Francesca Rollo, Renato Covello, Francesco Mazzola and Antonello Vidiri

Appl. Sci. 2022, 12(14), 7244; https://0-doi-org.brum.beds.ac.uk/10.3390/app12147244 - 19 Jul 2022

Cited by 4 | Viewed by 1500

Abstract

Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50th percentile of D_PT, the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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14 pages, 4346 KiB

Open AccessArticle

Automatic Screening of the Eyes in a Deep-Learning–Based Ensemble Model Using Actual Eye Checkup Optical Coherence Tomography Images

by Masakazu Hirota, Shinji Ueno, Taiga Inooka, Yasuki Ito, Hideo Takeyama, Yuji Inoue, Emiko Watanabe and Atsushi Mizota

Appl. Sci. 2022, 12(14), 6872; https://0-doi-org.brum.beds.ac.uk/10.3390/app12146872 - 07 Jul 2022

Viewed by 1085

Abstract

Eye checkups have become increasingly important to maintain good vision and quality of life. As the population requiring eye checkups increases, so does the clinical work burden of clinicians. An automatic screening algorithm to reduce the clinicians’ workload is necessary. Machine learning (ML) has recently become one of the chief techniques for automated image recognition and is a helpful tool for identifying ocular diseases. However, the accuracy of ML models is lower in a clinical setting than in the laboratory. The performance of ML models depends on the training dataset. Eye checkups often prioritize speed and minimize image processing. Data distribution differs from the training dataset and, consequently, decreases prediction performance. The study aim was to investigate an ML model to screen for retinal diseases from low-quality optical coherence tomography (OCT) images captured during actual eye chechups to prevent a dataset shift. The ensemble model with convolutional neural networks (CNNs) and random forest models showed high screening performance in the single-shot OCT images captured during the actual eye checkups. Our study indicates the strong potential of the ensemble model combining the CNN and random forest models in accurately predicting abnormalities during eye checkups. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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16 pages, 2688 KiB

Open AccessArticle

Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features

by Riccardo Scheda and Stefano Diciotti

Appl. Sci. 2022, 12(13), 6681; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136681 - 01 Jul 2022

Cited by 16 | Viewed by 5368

Abstract

SHAP (Shapley additive explanations) is a framework for explainable AI that makes explanations locally and globally. In this work, we propose a general method to obtain representative SHAP values within a repeated nested cross-validation procedure and separately for the training and test sets of the different cross-validation rounds to assess the real generalization abilities of the explanations. We applied this method to predict individual age using brain complexity features extracted from MRI scans of 159 healthy subjects. In particular, we used four implementations of the fractal dimension (FD) of the cerebral cortex—a measurement of brain complexity. Representative SHAP values highlighted that the most recent implementation of the FD had the highest impact over the others and was among the top-ranking features for predicting age. SHAP rankings were not the same in the training and test sets, but the top-ranking features were consistent. In conclusion, we propose a method—and share all the source code—that allows a rigorous assessment of the SHAP explanations of a trained model in a repeated nested cross-validation setting. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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10 pages, 1031 KiB

Open AccessArticle

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

by Gianluca Carlini, Nico Curti, Silvia Strolin, Enrico Giampieri, Claudia Sala, Daniele Dall’Olio, Alessandra Merlotti, Stefano Fanti, Daniel Remondini, Cristina Nanni, Lidia Strigari and Gastone Castellani

Appl. Sci. 2022, 12(12), 5946; https://0-doi-org.brum.beds.ac.uk/10.3390/app12125946 - 10 Jun 2022

Cited by 5 | Viewed by 1672

Abstract

Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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26 pages, 4061 KiB

Open AccessArticle

An Optimization-Based Diabetes Prediction Model Using CNN and Bi-Directional LSTM in Real-Time Environment

by Parul Madan, Vijay Singh, Vaibhav Chaudhari, Yasser Albagory, Ankur Dumka, Rajesh Singh, Anita Gehlot, Mamoon Rashid, Sultan S. Alshamrani and Ahmed Saeed AlGhamdi

Appl. Sci. 2022, 12(8), 3989; https://0-doi-org.brum.beds.ac.uk/10.3390/app12083989 - 14 Apr 2022

Cited by 45 | Viewed by 4872

Abstract

Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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13 pages, 4060 KiB

Open AccessFeature PaperArticle

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

by Mauro Iori, Carlo Di Castelnuovo, Laura Verzellesi, Greta Meglioli, Davide Giosuè Lippolis, Andrea Nitrosi, Filippo Monelli, Giulia Besutti, Valeria Trojani, Marco Bertolini, Andrea Botti, Gastone Castellani, Daniel Remondini, Roberto Sghedoni, Stefania Croci and Carlo Salvarani

Appl. Sci. 2022, 12(8), 3903; https://doi.org/10.3390/app12083903 - 12 Apr 2022

Cited by 10 | Viewed by 1948

Abstract

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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14 pages, 2629 KiB

Open AccessArticle

Psychological Stress Level Detection Based on Heartbeat Mode

by Dun Hu and Lifu Gao

Appl. Sci. 2022, 12(3), 1409; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031409 - 28 Jan 2022

Cited by 6 | Viewed by 2819

Abstract

The effective detection and quantification of mental health has always been an important research topic. Heart rate variability (HRV) analysis is a useful tool for detecting psychological stress levels. However, there is no consensus on the optimal HRV metrics in psychological assessments. This study proposes an HRV analysis method that is based on heartbeat modes to detect drivers’ stress. We used statistical tools for linguistics to detect and quantify the structure of the heart rate time series and summarized different heartbeat modes in the time series. Based on the k-nearest neighbors (k-NN) classification algorithm, the probability of each heartbeat mode was used as a feature to detect and recognize stress caused by the driving environment. The results indicated that the stress from the driving environment changed the heartbeat mode. Stress-related heartbeat modes were determined, facilitating detection of the stress state with an accuracy of 93.7%. We also concluded that the heartbeat mode was correlated to the galvanic skin response (GSR) signal, reflecting real-time abnormal mood fluctuations. The proposed method revealed HRV characteristics that made quantifying and detecting different mental conditions possible. Thus, it would be feasible to achieve personalized analyses to further study the interaction between physiology and psychology. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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15 pages, 1455 KiB

Open AccessArticle

Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

by Francesca Lizzi, Camilla Scapicchio, Francesco Laruina, Alessandra Retico and Maria Evelina Fantacci

Appl. Sci. 2022, 12(1), 148; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010148 - 24 Dec 2021

Cited by 9 | Viewed by 2754

Abstract

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman’s rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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Review

Jump to: Research

18 pages, 2380 KiB

Open AccessFeature PaperReview

Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

by Miriam Santoro, Silvia Strolin, Giulia Paolani, Giuseppe Della Gala, Alessandro Bartoloni, Cinzia Giacometti, Ilario Ammendolia, Alessio Giuseppe Morganti and Lidia Strigari

Appl. Sci. 2022, 12(7), 3223; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073223 - 22 Mar 2022

Cited by 16 | Viewed by 7663

Abstract

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers. Full article

(This article belongs to the Special Issue Artificial Intelligence Developments in Healthcare: Diagnosis, Rehabilitation and Screening)

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