{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T02:22:25Z","timestamp":1772504545563,"version":"3.50.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031390586","type":"print"},{"value":"9783031390593","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-39059-3_22","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:01:37Z","timestamp":1690722097000},"page":"326-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Explainable Approach for\u00a0Early Parkinson Disease Detection Using Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2436-6835","authenticated-orcid":false,"given":"Lerina","family":"Aversano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3223-7032","authenticated-orcid":false,"given":"Mario L.","family":"Bernardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2403-8313","authenticated-orcid":false,"given":"Marta","family":"Cimitile","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8025-733X","authenticated-orcid":false,"given":"Martina","family":"Iammarino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2227-9778","authenticated-orcid":false,"given":"Antonella","family":"Madau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1335-5276","authenticated-orcid":false,"given":"Chiara","family":"Verdone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104312","volume":"132","author":"MP Adams","year":"2021","unstructured":"Adams, M.P., Rahmim, A., Tang, J.: Improved motor outcome prediction in Parkinson\u2019s disease applying deep learning to DaTscan SPECT images. Comput. Biol. Med. 132, 104312 (2021)","journal-title":"Comput. Biol. Med."},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Aversano, L., et al.: Thyroid disease treatment prediction with machine learning approaches. Procedia Comput. Sci. 192, 1031\u20131040 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.08.106, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050921015945. knowledge-Based and Intelligent Information and Engineering Systems: International Conference KES2021","DOI":"10.1016\/j.procs.2021.08.106"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M., Montano, D., Verdone, C.: Using machine learning for early prediction of heart disease. In: 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 1\u20138. IEEE (2022)","DOI":"10.1109\/EAIS51927.2022.9787720"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., Iammarino, M., Verdone, C.: An enhanced UNet variant for effective lung cancer detection. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892757"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., Pecori, R.: Early detection of Parkinson disease using deep neural networks on gait dynamics. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207380"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., Pecori, R.: Fuzzy neural networks to detect Parkinson disease. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/FUZZ48607.2020.9177948"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"108135","DOI":"10.1016\/j.patcog.2021.108135","volume":"120","author":"L Aversano","year":"2021","unstructured":"Aversano, L., Bernardi, M.L., Cimitile, M., Pecori, R.: Deep neural networks ensemble to detect COVID-19 from CT scans. Pattern Recogn. 120, 108135 (2021)","journal-title":"Pattern Recogn."},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.nicl.2016.07.004","volume":"12","author":"S Badoud","year":"2016","unstructured":"Badoud, S., Van De Ville, D., Nicastro, N., Garibotto, V., Burkhard, P.R., Haller, S.: Discriminating among degenerative Parkinsonisms using advanced 123i-ioflupane SPECT analyses. NeuroImage: Clin. 12, 234\u2013240 (2016)","journal-title":"NeuroImage: Clin."},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Banerjee, P., Banerjee, S., Barnwal, R.P.: Explaining deep-learning models using gradient-based localization for reliable tea-leaves classifications. In: 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC), pp. 1\u20136 (2022). https:\/\/doi.org\/10.1109\/ICAECC54045.2022.9716699","DOI":"10.1109\/ICAECC54045.2022.9716699"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Benamer, H.T., et al.: Accurate differentiation of parkinsonism and essential tremor using visual assessment of [123i]-FP-CIT SPECT imaging: the [123i]-FP-CIT study group. Mov. Disord. Official J. Mov. Disord. Soc. 15, 503\u2013510(2000)","DOI":"10.1002\/1531-8257(200005)15:3<503::AID-MDS1013>3.0.CO;2-V"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Brown, E., et al.: Parkinson\u2019s progression markers initiative (PPMI) online expands biomarker research in Parkinson\u2019s disease (PD). Neurology, 98 (2022)","DOI":"10.1212\/WNL.98.18_supplement.1529"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.3389\/fninf.2018.00053","volume":"12","author":"D Castillo-Barnes","year":"2018","unstructured":"Castillo-Barnes, D., Ram\u00edrez, J., Segovia, F., Mart\u00ednez-Murcia, F.J., Salas-Gonzalez, D., G\u00f3rriz, J.M.: Robust ensemble classification methodology for i123-ioflupane SPECT images and multiple heterogeneous biomarkers in the diagnosis of Parkinson\u2019s disease. Front. Neuroinform. 12, 53 (2018)","journal-title":"Front. Neuroinform."},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Choi, H., Ha, S., Im, H.J., Paek, S.H., Lee, D.S.: Refining diagnosis of Parkinson\u2019s disease with deep learning-based interpretation of dopamine transporter imaging. NeuroImage: Clin. 16, 586\u2013594 (2017). https:\/\/doi.org\/10.1016\/j.nicl.2017.09.010, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2213158217302243","DOI":"10.1016\/j.nicl.2017.09.010"},{"issue":"1","key":"22_CR14","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40035-018-0123-y","volume":"7","author":"G Du","year":"2018","unstructured":"Du, G., Zhuang, P., Hallett, M., Zhang, Y.Q., Li, J.Y., Li, Y.J.: Properties of oscillatory neuronal activity in the basal ganglia and thalamus in patients with Parkinson\u2019s disease. Transl. Neurodegener. 7(1), 17 (2018). https:\/\/doi.org\/10.1186\/s40035-018-0123-y","journal-title":"Transl. Neurodegener."},{"key":"22_CR15","doi-asserted-by":"publisher","unstructured":"Siva Shankar, G., Manikandan, K.: Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization. Pattern Recogn. Lett. 125, 432\u2013438 (2019). https:\/\/doi.org\/10.1016\/j.patrec.2019.06.005, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167865519301734","DOI":"10.1016\/j.patrec.2019.06.005"},{"key":"22_CR16","unstructured":"G\u00f3rski, L., Ramakrishna, S., Nowosielski, J.M.: Towards grad-cam based explainability in a legal text processing pipeline. CoRR abs\/2012.09603 (2020). https:\/\/arxiv.org\/abs\/2012.09603"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/fnagi.2020.00004","volume":"12","author":"A Iarkov","year":"2020","unstructured":"Iarkov, A., Barreto, G.E., Grizzell, J.A., Echeverria, V.: Strategies for the treatment of Parkinson\u2019s disease: beyond dopamine. Front. Aging Neurosci. 12, 4 (2020)","journal-title":"Front. Aging Neurosci."},{"key":"22_CR18","doi-asserted-by":"publisher","unstructured":"Karayilan, T., Kilic, O.: Prediction of heart disease using neural network. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 719\u2013723 (2017). https:\/\/doi.org\/10.1109\/UBMK.2017.8093512","DOI":"10.1109\/UBMK.2017.8093512"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Khachnaoui, H., Mabrouk, R., Khlifa, N.: Machine learning and deep learning for clinical data and PET\/SPECT imaging in Parkinson\u2019s disease: a review. IET Image Process. 14(16), 4013\u20134026 (2020). https:\/\/doi.org\/10.1049\/iet-ipr.2020.1048, https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/abs\/10.1049\/iet-ipr.2020.1048","DOI":"10.1049\/iet-ipr.2020.1048"},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on mri. Zeitschrift f\u00fcr Medizinische Physik 29(2), 102\u2013127 (2019). https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0939388918301181, special Issue: Deep Learning in Medical Physics","DOI":"10.1016\/j.zemedi.2018.11.002"},{"key":"22_CR21","doi-asserted-by":"publisher","unstructured":"Nazari, M., et al.: Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI. Sci. Rep. 11(1), 22932 (2021). https:\/\/doi.org\/10.1038\/s41598-021-02385-x, https:\/\/doi.org\/10.1038\/s41598-021-02385-x","DOI":"10.1038\/s41598-021-02385-x"},{"issue":"4","key":"22_CR22","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1007\/s00259-021-05569-9","volume":"49","author":"M Nazari","year":"2022","unstructured":"Nazari, M., et al.: Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain Parkinsonian syndromes. Eur. J. Nucl. Med. Mol. Imaging 49(4), 1176\u20131186 (2022). https:\/\/doi.org\/10.1007\/s00259-021-05569-9","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"6","key":"22_CR23","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1007\/s00259-017-3918-7","volume":"45","author":"FP Oliveira","year":"2018","unstructured":"Oliveira, F.P., Faria, D.B., Costa, D.C., Castelo-Branco, M., Tavares, J.M.R.: Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson\u2019s disease based on [123i] FP-CIT SPECT images. Eur. J. Nucl. Med. Mol. Imaging 45(6), 1052\u20131062 (2018)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"22_CR24","doi-asserted-by":"publisher","unstructured":"Ortiz, A., Munilla, J., Mart\u00ednez-Iba\u00f1ez, M., G\u00f3rriz, J.M., Ram\u00edrez, J., Salas-Gonzalez, D.: Parkinson\u2019s disease detection using isosurfaces-based features and convolutional neural networks. Front. Neuroinform. 13, 48 (2019). https:\/\/doi.org\/10.3389\/fninf.2019.00048, https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2019.00048","DOI":"10.3389\/fninf.2019.00048"},{"issue":"1","key":"22_CR25","doi-asserted-by":"publisher","first-page":"17013","DOI":"10.1038\/nrdp.2017.13","volume":"3","author":"W Poewe","year":"2017","unstructured":"Poewe, W., et al.: Parkinson disease. Nat. Rev. Dis. Primers 3(1), 17013 (2017). https:\/\/doi.org\/10.1038\/nrdp.2017.13","journal-title":"Nat. Rev. Dis. Primers"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Selvaraju, R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. 2016. arXiv preprint arXiv:1610.02391 (2016)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"5","key":"22_CR27","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s007020070077","volume":"107","author":"W Staffen","year":"2000","unstructured":"Staffen, W., Mair, A., Unterrainer, J., Trinka, E., Ladurner, G.: Measuring the progression of idiopathic Parkinson\u2019s disease with [123i] $$beta$$-CIT SPECT. J. Neural Transm. 107(5), 543\u2013552 (2000)","journal-title":"J. Neural Transm."},{"key":"22_CR28","doi-asserted-by":"publisher","unstructured":"Valizadeh, M., Wolff, S.J.: Convolutional neural network applications in additive manufacturing: a review. Adv. Ind. Manuf. Eng. 4, 100072 (2022). https:\/\/doi.org\/10.1016\/j.aime.2022.100072, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666912922000046","DOI":"10.1016\/j.aime.2022.100072"},{"key":"22_CR29","doi-asserted-by":"publisher","unstructured":"Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf. Fusion 76, 89\u2013106 (2021). https:\/\/doi.org\/10.1016\/j.inffus.2021.05.009, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521001093","DOI":"10.1016\/j.inffus.2021.05.009"},{"key":"22_CR30","doi-asserted-by":"publisher","unstructured":"Vuppala, S.K., Behera, M., Jack, H., Bussa, N.: Explainable deep learning methods for medical imaging applications. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 334\u2013339 (2020). https:\/\/doi.org\/10.1109\/ICCCA49541.2020.9250820","DOI":"10.1109\/ICCCA49541.2020.9250820"},{"issue":"13","key":"22_CR31","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1007\/s00259-019-04502-5","volume":"46","author":"M Wenzel","year":"2019","unstructured":"Wenzel, M., et al.: Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics. Eur. J. Nucl. Med. Mol. Imaging 46(13), 2800\u20132811 (2019)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"4","key":"22_CR32","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611\u2013629 (2018). https:\/\/doi.org\/10.1007\/s13244-018-0639-9","journal-title":"Insights Imaging"},{"issue":"5","key":"22_CR33","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/s10278-016-9910-0","volume":"30","author":"YC Zhang","year":"2017","unstructured":"Zhang, Y.C., Kagen, A.C.: Machine learning interface for medical image analysis. J. Digit. Imaging 30(5), 615\u2013621 (2017)","journal-title":"J. Digit. Imaging"},{"key":"22_CR34","doi-asserted-by":"publisher","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.319, https:\/\/doi.ieeecomputersociety.org\/10.1109\/CVPR.2016.319","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39059-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T03:12:35Z","timestamp":1702869155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39059-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031390586","9783031390593"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39059-3_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}