INTOCADS Laboratory – Intelligent Optimized Computer-Aided Diagnosis Systems for Bioengineers Laboratory

Lab Team

Lab Mission

Setting up the INTelligent Optimized Computer-Aided Diagnosis Systems for bioengineers Laboratory (short INTOCADS) – AREA 4: Data and computational modelling. The laboratory will include a complete facility to design, implement and validate optimized decision support systems by means of the following workflow (materials and methods):
1) acquisition of biosignals and supply of bioimages and biodata for data-driven digital twins to simulate and estimate patient-specific characteristics of diseases;
2) advanced and intelligent techniques for processing and segmenting the available materials (biosignals,
bioimages, biodata and biomarkers) and forming the biopatterns;
3) intelligent and optimized methodologies for multifactorial and combined biopattern identification
(radiomic and radiogenomic feature extraction, reduction and transformation);
4) modelling, implementation, training and validation of machine/deep learning optimized topology for
providing Decision Support and Computer Aided Decision/Diagnosis Systems;
5) classification, characterization, staging and grading of pathologies (chronic, acute, neurodegenerative), or motor disorders.

Link: https://labinfind.poliba.it/ricerca-scientifica/pnrr/brief/intocads/

Scientific Publications

Brunetti, A., Buongiorno, D., Altini, N., Bevilacqua, V. Enabling Technologies for Optimized Diagnosis, Therapy and Rehabilitation: Obtained Results and Future Works. In: Bochicchio, M., Siciliano, P., Monteriù, A., Bettelli, A., De Fano, D. (eds) Ambient Assisted Living. ForItAAL 2023. Lecture Notes in Bioengineering. Springer, Cham. doi: 10.1007/978-3-031-63913-5_19

Zaccaria, G. M., Altini, N., Mezzolla, G., Vegliante, M. C., Stranieri, M., Pappagallo, S. A., Ciavarella, S., Guarini, A., Bevilacqua, V. SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data. Computer Methods and Programs in Biomedicine, 2024, 244, p.107966. doi: 10.1016/j.cmpb.2023.107966

Suglia, V., Palazzo, L., Bevilacqua, V., Passantino, A., Pagano, G., D’Addio, G. A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors. Sensors, 2024, 24, 2199. doi: 10.3390/s24072199

Altini, N., Rossini, M., Turkevi-Nagy, S., Pesce, F., Pontrelli, P., Prencipe, B., Berloco, F., Seshan, S., Gibier, J. B., Dorado, A. P., Bueno, G., Peruzzi, L., Rossi, M., Eccher, A., Li, F., Koumpis, A., Beyan, O., Barratt, J., Huy Quoc, V., Mohan, C., Van Nguyen, H., Cicalese, P. A., Ernst, A., Gesualdo, L., Bevilacqua, V., Becker, J. U. Performance and Limitations of a Supervised Deep Learning Approach for the Histopathological Oxford Classification of Glomeruli with IgA Nephropathy. Computer Methods and Programs in Biomedicine, 2023, p.107814. doi: 10.1016/j.cmpb.2023.107814

Gentile, E., Brunetti, A., Ricci, K., Vecchio, E., Santoro, C., Sibilano, E., Bevilacqua, V., Iliceto, G., Craighero, L., de Tommaso, M. Effects of movement congruence on motor resonance in early Parkinson’s disease. Scientific Reports, 2023, 13(1), p.14887. doi: 10.1038/s41598-023-42112-2

Prencipe, B., Delprete, C., Garolla, E., Corallo, F., Gravina, M., Natalicchio, M. I., Buongiorno, D., Bevilacqua, V., Altini, N., Brunetti, A. An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients. Bioengineering. 2023; 10(7):747. doi: 10.3390/bioengineering10070747

Sibilano, E., Brunetti, A., Buongiorno, D., Lassi, M., Grippo, A., Bessi, V., Micera, S., Mazzoni, A., Bevilacqua, V. An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG. J Neural Eng. 2023 Feb 6. doi: 10.1088/1741-2552/acb96e

Zaccaria G.M., Berloco, F., Buongiorno, D., Brunetti, A., Altini, N., Bevilacqua, V. A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma. Computer Methods and Programs in Biomedicine, 2024, p.108408. doi: 10.1016/j.cmpb.2024.108408

Sibiliano, E., Buongiorno, D., Lassi, M., Grippo, A., Bessi, V., Sorbi, S., Mazzoni, A., Bevilacqua, V., Brunetti, A. Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG, 2024, 28(6):3422-3433. doi: 10.1109/JBHI.2024.3390606

Berloco, F., Marvulli, P.M., Suglia, V., Colucci, S., Pagano, G., Palazzo, L., Aliani., M., Castellana, G., Guido, P., D’Addio, G., Bevilacqua, V. Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare. Applied Sciences, 2024, 14(14), 6084. doi: 10.3390/app14146084

Clemente, L., La Rocca, M., Paparella, G., Delussi, M., Tancredi, G., Ricci, K., Procida, G., Introna, A., Brunetti, A., Taurisano, P., Bevilacqua, V., de Tommaso, M. Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain—Computer Interface Study, 2024, Sensors, 24(7), 2329. doi: 10.3390/s24072329

Berloco, F., Zaccaria G.M., Altini, N., Colucci, S., Bevilacqua, V. A Multimodal Framework for Assessing the Link between Pathomics, Transcriptomics, and Pancreatic Cancer Mutations. Computerized Medical Imaging and Graphics, 2025, p. 102526. doi: 10.1016/j.compmedimag.2025.102526

Zaccaria G.M., Altini, N., Mongelli, V., Marino, F., Bevilacqua, V. Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma. International Journal of Medical Informatics, 2025, p. 105883. doi: 10.1016/j.ijmedinf.2025.105883

Suglia, V., Brunetti, A., Pasquini, G., Caputo, M., Marvulli, T. M., Sibilano, E., Della Bella, S., Carrozza, P., Beni, C., Naso, D., Monaco, V., Cristella, G., Bevilacqua, V., Buongiorno, D. A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality. Sensors, 2023, 23(11), 5017. doi: 10.3390/s23115017

Palazzo, L., Suglia, V., Grieco, S., Buongiorno, D., Brunetti, A., Carnimeo, L., Amitrano, F., Coccia, A., Pagano, G., D’Addio, G., Bevilacqua, V. A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors. Sensors, 2025, 25, 260. doi: 10.3390/s25010260

Altini, N., Lasaracina, E., Galeone, F., Prunella, M., Suglia, V., Carnimeo, L., Triggiani, V., Ranieri, D., Brunetti, G., Bevilacqua, V. A Comparison Between Unimodal and Multimodal Segmentation Models for Deep Brain Structures from T1- and T2-Weighted MRI. Machine Learning and Knowledge Extraction, 2025, 7(3), 84. doi: 10.3390/make7030084

Prunella, M., Altini, N., D’Alessandro, R., Schirizzi, A., Ricci, A. D., Lotesoriere, C., Scarabaggio, P., Carli, R., Dotoli, M., Giannelli, G., Bevilacqua, V. Pharmacometric and Digital Twin modeling for adaptive scheduling of combination therapy in advanced gastric cancer. Computer Methods and Programs in Biomedicine, 2025, 108919. doi: 10.1016/j.cmpb.2025.108919

Prunella, M., Altini, N., D’Alessandro, R., Schirizzi, A., De Leonardis, G., Arborea, G., Savino, M. T., Valentini, A. M., Armentano, R., Ricci, A. D., Lotesoriere, C., Carli, R., Dotoli, M., Giannelli, G., Bevilacqua, V. Automated Pathomic Analysis of Angiogenesis and Immune Profiles Unveils an Interpretable Prognostic Biomarker in Colon and Gastric Cancers. IEEE Journal of Biomedical and Health Informatics, 2025. doi: 10.1109/JBHI.2025.3625431

Altini, N., Prunella, M., Seshan, S. V., Sciascia, S., Barreca, A., Del Gobbo, A., Porubsky, S., Nguyen, H. V., Delprete, C., Prencipe, B., Dobi, D., van Doorn, D. P. C., Timmermans, S. A. M. E. G., van Paassen, P., Bevilacqua, V., Becker, J. U. Multistain multicompartment automatic segmentation in renal biopsies with thrombotic microangiopathies and other vasculopathies. Computerized Medical Imaging and Graphics, 2025, 126, 102658. doi: 10.1016/j.compmedimag.2025.102658

Marvulli, P. M., Buongiorno, D., Zaccaria, G. M., Brunetti, A., Marino, F., & Bevilacqua, V. Multimodal framework for TACE treatment response prediction in patients with hepatocellular carcinoma. Computerized Medical Imaging and Graphics, 2025, 102679. doi: 10.1016/j.compmedimag.2025.102679

Prunella, M., Altini, N., Zaccaria, G. M., Scarabaggio, P., Carli, R., Dotoli, M., Bevilacqua, V. Pharmacokinetic-pharmacodynamic modeling of tumor angiogenesis unveils the need for time-adjustable drug regimens. IX National Congress of Bioengineering, June 16–18, 2025, Palermo, Italy. Link

Scardigno, R. M.; Buongiorno, D.; Brunetti, A.; Suglia, V.; Della Bella, S.; Beni, C.; Lonoce, N.; Carrozza, P.; Carli, R.; Dotoli, M.; Pasquini, G.; Cristella, G.; Bevilacqua, V. A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Reaching Tasks in Virtual Reality. IX National Congress of Bioengineering, June 16–18, 2025, Palermo, Italy. Link

Suglia, V.; Camardella, C.; Rinaldi, G.; Chiaradia, D.; Buongiorno, D.; Zhou, H.; Frisoli, A.; Leonardis, D.; Bevilacqua, V. Muscle Network Analysis of a Dynamic Bilateral Task With an Upper Limb Exoskeleton. 2025 International Conference On Rehabilitation Robotics (ICORR). IEEE, 2025. p. 419-424. doi: 10.1109/ICORR66766.2025.11063072

Palazzo, L.; Suglia, V.; Grieco, S.; Buongiorno, D.; Pagano, G.; Bevilacqua, V.; D’Addio, G. Optimized Deep Learning-based Pathological Gait Recognition explored through Network Analysis of Inertial Data. 2025 IEEE Medical Measurements & Applications (MeMeA). IEEE, 2025. p. 1-5. doi: 10.1109/MeMeA65319.2025.11067994

Brunetti A., Zaccaria G.M., Sibilano E., Marzi S., Vidiri A., Bevilacqua V. Development and Independent Validation of Explainable Radiomics-based Machine Learning Models for Prognosis in Colorectal Liver Metastases. Frontiers in Digital Health, 2026, volume 7, doi: 10.3389/fdgth.2025.1752699

Suglia V., Camardella C., Rinaldi G., Chiaradia D., Buongiorno D., Leonardis D., Zhou H., Frisoli A., Bevilacqua V. Muscle networks analysis on an active occupational shoulder exoskeleton. Biomedical Signal Processing and Control, 2026, Volume 119, Part A, 109653. doi: 10.1016/j.bspc.2026.109653

Mancazzo, V., Sibilano, E., Buongiorno, D., Carli, R., Bevilacqua, V., Brunetti, A. Parkinson’s disease detection from resting-state EEG using a Transformer model with Multi-Head Attention explanations. Biomedical Signal Processing and Control, 2026, Volume 119, Part A, 109811. doi: 10.1016/j.bspc.2026.109811

Devices 

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