Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.

This paper introduces a novel semi-supervised neuro-fuzzy system to predict ICU admissions among post-COVID organ transplant recipients. Addressing the challenges of small sample sizes and lacking labels in organ transplantation, our study takes on these issues by proposing a DBN-Based Dual Manifold Regularized Fuzzy Broad Learning System (D-DMR-FBLS). This system utilizes the streamlined and flat architecture of the Broad Learning System (BLS), integrating Deep Belief Networks (DBN) and Takagi-Sugeno-Kang (TSK) systems to enhance representation learning capacities during the Unsupervised Training Phase (UTP). The system combines the strong feature learning capabilities of DBN with the powerful fuzzy rule extraction capacity of the TSK system, enhancing the model’s predictive performance and generalization capability. Moreover, we propose two types of graph-based manifold regularization, sample-based and feature-based, within this novel D-DMR-FBLS framework. Our method enhances its predictive ability by exploiting both the similarity among unlabeled and labeled patient samples, as well as the correlations between features within the fuzzy feature space. Employed to predict ICU admission risks in post-transplant COVID-19 patients, the method has demonstrated superior performance over existing methods, particularly in scenarios with limited samples and labels, thereby providing more accurate decision support for medical professionals in optimizing resource allocation for transplant patients., This paper is part of project PID2022-143299OB-I00, financed by MCIN/AEI/10.13030/501100011033/FEDER,UE., Peer Reviewed

This paper introduces a novel semi-supervised Dual-Manifold Regularized Fuzzy Broad Learning System (DMR-FBLS) designed for predicting intensive care unit (ICU) admissions among post-COVID organ transplant recipients. Addressing the challenge of increased mortality risk and strained medical resources among patients who are COVID-19-infected transplant recipients, our study fills the gap by employing machine learning to predict the ICU requirements for this particular group of patients. The DMR-FBLS uniquely integrates two manifold regularizations - feature and sample manifold regularization - into the fuzzy broad learning system (FBLS) framework. This integration enhances the system's capacity to utilize the latent structural relationships within the data. Evaluated using the IDOTCOVID database, the DMR-FBLS demonstrated superior performance over other models in terms of accuracy, F1-score, and G-mean. In scenarios with limited sample availability, DMR-FBLS outperformed both the FBLS and TSK fuzzy systems in terms of accuracy and G-mean. This underscores the efficacy of DMR-FBLS as a semi-supervised learning fuzzy system, accurately predicting ICU demands and thereby providing decision support for medical professionals in optimizing resource allocation for transplant patients., This research has been supported by the project PID2022-143299OB-I00., Peer Reviewed

Optical Coherence Tomography Angiography (OCTA) has emerged in the last decade as a reference imaging technology that offers a non-invasive, rapid method for the detailed assessment of microvascular changes at the capillary level. In this brief study, we assess, using statistical and Machine Learning models, the usefulness of a vascular tortuosity-based OCTA image transformation for the diagnostic discrimination between Type 1 Diabetes Mellitus patients with mild or no presence of Diabetic Retinopathy (DR), and those with advanced DR., This study was funded by Spanish research grant PID2022-143299OB-I00/AEI/10.13039/501100011033/FEDER, UE., Peer Reviewed

Data quality control is fundamental in data-driven analysis with machine learning (ML) models. In the domain of drug research, there is an increasing interest in the prediction of relevant biocompounds physicochemical properties with ML. In order to build predictive models of good quality, it is important to adequately select representative datasets. In this work, we combine ML prediction and Self-Organizing Maps-based exploration to build an interpretable machine learning model and to characterize those data that are most difficult to predict in the validation stage., Peer Reviewed

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging., This research was funded by H2020-EU.1.3.—EXCELLENT SCIENCE—Marie Skłodowska-Curie Actions, grant number H2020-MSCA-ITN-2018-813120; Proyectos de investigación en salud 2020, grant number PI20/00064. PID2019-104551RB-I00; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN (http://www.ciber-bbn.es/en, accessed on 3 November 2023), CB06/01/0010), an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU Fondo Europeo de Desarrollo Regional (FEDER); Spanish Agencia Española de Investigación (AEI) PID2022-143299OB-I00 grant; XartecSalut 2021-XARDI-00021. Carla Pitarch is a fellow of Eurecat’s
“Vicente López” Ph.D. grant program., Peer Reviewed

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects—thereby regulating the protein’s activity—unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights., This work was funded by Spanish PID2022-143299OB-I00/AEI/10.13039/501100011033/FEDER, UE, research project., Peer Reviewed

Purpose: To develop a machine learning (ML) algorithm capable of determining cardiovascular (CV) risk in multimodal retinal images from patients with type 1 diabetes mellitus (T1DM), distinguishing between moderate, high, and very high-risk levels.
Design: Cross-sectional analysis of a retinal image data set from a previous prospective OCT angiography (OCTA) study (ClinicalTrials.gov NCT03422965).
Participants: Patients with T1DM included in the progenitor study.
Methods: Radiomic features were extracted from color fundus photographs (CFPs), OCT, and OCTA images, and ML models were trained using these features either individually or combined with clinical data (demographics and systemic data, OCT + OCTA commercial software metrics, ocular data, blood data). Different data combinations were tested to determine the CV risk stages, defined according to international classifications.
Main Outcome Measures: Area under the receiver operating characteristic curve mean and standard deviation for each ML model and each data combination.
Results: A data set of 597 eyes (359 individuals) was analyzed. Models trained only with the radiomic features achieved area under the curve (AUC) values of (0.79 ± 0.03) to identify moderate risk cases from high and very high-risk cases, and (0.73 ± 0.07) for distinguishing between high and very high-risk cases. The addition of clinical variables improved all AUC values, obtaining (0.99 ± 0.01) for identifying moderate cases, and (0.95 ± 0.02) for differentiating between high and very high-risk cases. For very high CV risk, radiomics combined with OCT + OCTA metrics and ocular data achieved an AUC of (0.89 ± 0.02) without systemic data input. The performance of the models was similar in unilateral and bilateral eye image data sets.
Conclusions: Radiomic features obtained from retinal images are helpful to discriminate and classify CV risk labels, differentiating risk categories. The addition of demographics and systemic data combined with ocular data differentiate high from very high CV risk cases, and interestingly OCT + OCTA metrics with ocular data identify very high CV risk cases without systemic data input. These results reflect the potential of this oculomics approach for CV risk assessment.
Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., This study was funded by Instituto de Salud Carlos III through the project PI21/01384 and co-funded by the European Union (PI: J.Z.-V.), and supported by the Spanish research grant (PID2022-143299OB-I00/AEI/ 10.13039/501100011033/FEDER, UE [A.T.-D., E.R., and A.V.]); Generalitat de Catalunya (grant Grup de Recerca SGR-Cat2021 with reference 2021SGR-01411 [F.M.]); and the Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033, Spain (grant no.: PID2023- 147469NB-C21 [F.M.])., Peer Reviewed

This paper introduces a novel semi-supervised neuro-fuzzy system to predict ICU admissions among post-COVID organ transplant recipients. Addressing the challenges of small sample sizes and lacking labels in organ transplantation, our study takes on these issues by proposing a DBN-Based Dual Manifold Regularized Fuzzy Broad Learning System (D-DMR-FBLS). This system utilizes the streamlined and flat architecture of the Broad Learning System (BLS), integrating Deep Belief Networks (DBN) and Takagi-Sugeno-Kang (TSK) systems to enhance representation learning capacities during the Unsupervised Training Phase (UTP). The system combines the strong feature learning capabilities of DBN with the powerful fuzzy rule extraction capacity of the TSK system, enhancing the model’s predictive performance and generalization capability. Moreover, we propose two types of graph-based manifold regularization, sample-based and feature-based, within this novel D-DMR-FBLS framework. Our method enhances its predictive ability by exploiting both the similarity among unlabeled and labeled patient samples, as well as the correlations between features within the fuzzy feature space. Employed to predict ICU admission risks in post-transplant COVID-19 patients, the method has demonstrated superior performance over existing methods, particularly in scenarios with limited samples and labels, thereby providing more accurate decision support for medical professionals in optimizing resource allocation for transplant patients., This paper is part of project PID2022-143299OB-I00, financed by MCIN/AEI/10.13030/501100011033/FEDER,UE., Peer Reviewed, Postprint (published version)

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging., This research was funded by H2020-EU.1.3.—EXCELLENT SCIENCE—Marie Skłodowska-Curie Actions, grant number H2020-MSCA-ITN-2018-813120; Proyectos de investigación en salud 2020, grant number PI20/00064. PID2019-104551RB-I00; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN (http://www.ciber-bbn.es/en, accessed on 3 November 2023), CB06/01/0010), an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU Fondo Europeo de Desarrollo Regional (FEDER); Spanish Agencia Española de Investigación (AEI) PID2022-143299OB-I00 grant; XartecSalut 2021-XARDI-00021. Carla Pitarch is a fellow of Eurecat’s
“Vicente López” Ph.D. grant program., Peer Reviewed, Postprint (published version)

Optical Coherence Tomography Angiography (OCTA) has emerged in the last decade as a reference imaging technology that offers a non-invasive, rapid method for the detailed assessment of microvascular changes at the capillary level. In this brief study, we assess, using statistical and Machine Learning models, the usefulness of a vascular tortuosity-based OCTA image transformation for the diagnostic discrimination between Type 1 Diabetes Mellitus patients with mild or no presence of Diabetic Retinopathy (DR), and those with advanced DR., This study was funded by Spanish research grant PID2022-143299OB-I00/AEI/10.13039/501100011033/FEDER, UE., Peer Reviewed, Postprint (author's final draft)

This paper introduces a novel semi-supervised Dual-Manifold Regularized Fuzzy Broad Learning System (DMR-FBLS) designed for predicting intensive care unit (ICU) admissions among post-COVID organ transplant recipients. Addressing the challenge of increased mortality risk and strained medical resources among patients who are COVID-19-infected transplant recipients, our study fills the gap by employing machine learning to predict the ICU requirements for this particular group of patients. The DMR-FBLS uniquely integrates two manifold regularizations - feature and sample manifold regularization - into the fuzzy broad learning system (FBLS) framework. This integration enhances the system's capacity to utilize the latent structural relationships within the data. Evaluated using the IDOTCOVID database, the DMR-FBLS demonstrated superior performance over other models in terms of accuracy, F1-score, and G-mean. In scenarios with limited sample availability, DMR-FBLS outperformed both the FBLS and TSK fuzzy systems in terms of accuracy and G-mean. This underscores the efficacy of DMR-FBLS as a semi-supervised learning fuzzy system, accurately predicting ICU demands and thereby providing decision support for medical professionals in optimizing resource allocation for transplant patients., This research has been supported by the project PID2022-143299OB-I00., Peer Reviewed, Postprint (author's final draft)

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects—thereby regulating the protein’s activity—unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights., This work was funded by Spanish PID2022-143299OB-I00/AEI/10.13039/501100011033/FEDER, UE, research project., Peer Reviewed, Postprint (published version)

Purpose: To develop a machine learning (ML) algorithm capable of determining cardiovascular (CV) risk in multimodal retinal images from patients with type 1 diabetes mellitus (T1DM), distinguishing between moderate, high, and very high-risk levels.
Design: Cross-sectional analysis of a retinal image data set from a previous prospective OCT angiography (OCTA) study (ClinicalTrials.gov NCT03422965).
Participants: Patients with T1DM included in the progenitor study.
Methods: Radiomic features were extracted from color fundus photographs (CFPs), OCT, and OCTA images, and ML models were trained using these features either individually or combined with clinical data (demographics and systemic data, OCT + OCTA commercial software metrics, ocular data, blood data). Different data combinations were tested to determine the CV risk stages, defined according to international classifications.
Main Outcome Measures: Area under the receiver operating characteristic curve mean and standard deviation for each ML model and each data combination.
Results: A data set of 597 eyes (359 individuals) was analyzed. Models trained only with the radiomic features achieved area under the curve (AUC) values of (0.79 ± 0.03) to identify moderate risk cases from high and very high-risk cases, and (0.73 ± 0.07) for distinguishing between high and very high-risk cases. The addition of clinical variables improved all AUC values, obtaining (0.99 ± 0.01) for identifying moderate cases, and (0.95 ± 0.02) for differentiating between high and very high-risk cases. For very high CV risk, radiomics combined with OCT + OCTA metrics and ocular data achieved an AUC of (0.89 ± 0.02) without systemic data input. The performance of the models was similar in unilateral and bilateral eye image data sets.
Conclusions: Radiomic features obtained from retinal images are helpful to discriminate and classify CV risk labels, differentiating risk categories. The addition of demographics and systemic data combined with ocular data differentiate high from very high CV risk cases, and interestingly OCT + OCTA metrics with ocular data identify very high CV risk cases without systemic data input. These results reflect the potential of this oculomics approach for CV risk assessment.
Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., This study was funded by Instituto de Salud Carlos III through the project PI21/01384 and co-funded by the European Union (PI: J.Z.-V.), and supported by the Spanish research grant (PID2022-143299OB-I00/AEI/ 10.13039/501100011033/FEDER, UE [A.T.-D., E.R., and A.V.]); Generalitat de Catalunya (grant Grup de Recerca SGR-Cat2021 with reference 2021SGR-01411 [F.M.]); and the Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033, Spain (grant no.: PID2023- 147469NB-C21 [F.M.])., Peer Reviewed, Postprint (published version)

Data quality control is fundamental in data-driven analysis with machine learning (ML) models. In the domain of drug research, there is an increasing interest in the prediction of relevant biocompounds physicochemical properties with ML. In order to build predictive models of good quality, it is important to adequately select representative datasets. In this work, we combine ML prediction and Self-Organizing Maps-based exploration to build an interpretable machine learning model and to characterize those data that are most difficult to predict in the validation stage., Peer Reviewed, Postprint (author's final draft)