Exploring explainable AI features in the vocal biomarkers of lung disease
Authors: 
Zhao Chen, Ning Liang, Haoyuan Li, Haili Zhang, Huizhen Li, Lijiao Yan, Ziteng Hu, Yaxin Chen, Yujing Zhang, Yanping Wang, Dandan Ke, Nannan ShiAuthors Info & Claims
References
[1]
J. Meghji, K. Mortimer, A. Agusti, B.W. Allwood, I. Asher, E.D. Bateman, K. Bissell, C.E. Bolton, A. Bush, B. Celli, Improving lung health in low-income and middle-income countries: from challenges to solutions, Lancet 397 (2021) 928–940,.
[2]
S. Safiri, K. Carson-Chahhoud, M. Noori, S.A. Nejadghaderi, M.J. Sullman, J.A. Heris, K. Ansarin, M.A. Mansournia, G.S. Collins, A.-A. Kolahi, Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: results from the Global Burden of Disease Study 2019, BMJ (2022) 378,.
[3]
S. Chen, M. Kuhn, K. Prettner, F. Yu, T. Yang, T. Bärnighausen, D.E. Bloom, C. Wang, The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020–50: a health-augmented macroeconomic modelling study, Lancet Global Health 11 (2023) e1183–e1193,.
[4]
S. Salvi, G.A. Kumar, R. Dhaliwal, K. Paulson, A. Agrawal, P.A. Koul, P. Mahesh, S. Nair, V. Singh, A.N. Aggarwal, The burden of chronic respiratory diseases and their heterogeneity across the states of India: the Global Burden of Disease Study 1990–2016, Lancet Global Health 6 (2018) e1363–e1374,.
[5]
A. Agusti, C.F. Vogelmeier, D.M. Halpin, Tackling the global burden of lung disease through prevention and early diagnosis, Lancet Respir. Med. 10 (2022) 1013–1015,.
[6]
T. Ferkol, D. Schraufnagel, The global burden of respiratory disease, Annals of the American Thoracic Society 11 (2014) 404–406,.
[7]
F. Pancaldi, M. Sebastiani, G. Cassone, F. Luppi, S. Cerri, G. Della Casa, A. Manfredi, Analysis of pulmonary sounds for the diagnosis of interstitial lung diseases secondary to rheumatoid arthritis, Comput. Biol. Med. 96 (2018) 91–97,.
[8]
Y. Belessis, B. Dixon, G. Hawkins, J. Pereira, J. Peat, R. MacDonald, P. Field, A. Numa, J. Morton, K. Lui, Early cystic fibrosis lung disease detected by bronchoalveolar lavage and lung clearance index, Am. J. Respir. Crit. Care Med. 185 (2012) 862–873,.
[9]
D.S. Watson, J. Krutzinna, I.N. Bruce, C.E. Griffiths, I.B. McInnes, M.R. Barnes, L. Floridi, Clinical applications of machine learning algorithms: beyond the black box, BMJ (2019) 364,.
[10]
N. Luo, X. Zhong, L. Su, Z. Cheng, W. Ma, P. Hao, Artificial intelligence-assisted dermatology diagnosis: from unimodal to multimodal, Comput. Biol. Med. (2023),.
[11]
R. Ranjbarzadeh, A. Caputo, E.B. Tirkolaee, S.J. Ghoushchi, M. Bendechache, Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools, Comput. Biol. Med. 152 (2023),.
[12]
W. Samek, T. Wiegand, K.-R. Müller, Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models, arXiv preprint arXiv:1708.08296 (2017),.
[13]
S. Ali, F. Akhlaq, A.S. Imran, Z. Kastrati, S.M. Daudpota, M. Moosa, The enlightening role of explainable artificial intelligence in medical & healthcare domains: a systematic literature review, Comput. Biol. Med. (2023),.
[14]
G. Vilone, L. Longo, Notions of explainability and evaluation approaches for explainable artificial intelligence, Inf. Fusion 76 (2021) 89–106,.
[15]
A.B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion 58 (2020) 82–115,.
[16]
D. Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, G.-Z. Yang, XAI—explainable artificial intelligence, Sci. Robot. 4 (2019),.
[17]
M. Ghassemi, L. Oakden-Rayner, A.L. Beam, The false hope of current approaches to explainable artificial intelligence in health care, The Lancet Digital Health 3 (2021) e745–e750,.
[18]
J.-B. Lamy, B. Sekar, G. Guezennec, J. Bouaud, B. Séroussi, Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach, Artif. Intell. Med. 94 (2019) 42–53,.
[19]
M. Chary, E.W. Boyer, M.M. Burns, Diagnosis of acute poisoning using explainable artificial intelligence, Comput. Biol. Med. 134 (2021),.
[20]
G. Fagherazzi, A. Fischer, M. Ismael, V. Despotovic, Voice for health: the use of vocal biomarkers from research to clinical practice, Digit. Biomark. 5 (2021) 78–88,.
[21]
J. Meena, Y. Hasija, Application of explainable artificial intelligence in the identification of Squamous Cell Carcinoma biomarkers, Comput. Biol. Med. 146 (2022),.
[22]
C.C. Yang, Explainable artificial intelligence for predictive modeling in healthcare, Journal of healthcare informatics research 6 (2022) 228–239,.
[23]
G. Novakovsky, N. Dexter, M.W. Libbrecht, W.W. Wasserman, S. Mostafavi, Obtaining genetics insights from deep learning via explainable artificial intelligence, Nat. Rev. Genet. 24 (2023) 125–137,.
[24]
J. Petch, S. Di, W. Nelson, Opening the black box: the promise and limitations of explainable machine learning in cardiology, Can. J. Cardiol. 38 (2022) 204–213,.
[25]
S. Reddy, Explainability and artificial intelligence in medicine, The Lancet Digital Health 4 (2022) e214–e215,.
[26]
K. Rasheed, A. Qayyum, M. Ghaly, A. Al-Fuqaha, A. Razi, J. Qadir, Explainable, trustworthy, and ethical machine learning for healthcare: a survey, Comput. Biol. Med. (2022),.
[27]
J.C. Mundt, A.P. Vogel, D.E. Feltner, W.R. Lenderking, Vocal acoustic biomarkers of depression severity and treatment response, Biol. Psychiatry 72 (2012) 580–587,.
[28]
B. Tracey, S. Patel, Y. Zhang, K. Chappie, D. Volfson, F. Parisi, C. Adans-Dester, F. Bertacchi, P. Bonato, P. Wacnik, Voice biomarkers of recovery from acute respiratory illness, IEEE Journal of Biomedical and Health Informatics 26 (2021) 2787–2795,.
[29]
N. Cummins, A. Baird, B.W. Schuller, Speech analysis for health: current state-of-the-art and the increasing impact of deep learning, Methods 151 (2018) 41–54,.
[30]
E.E. Mohamed, Voice changes in patients with chronic obstructive pulmonary disease, Egypt. J. Chest Dis. Tuberc. 63 (2014) 561–567,.
[31]
A.M. Saeed, N.M. Riad, N.M. Osman, A.N. Khattab, S.E. Mohammed, Study of voice disorders in patients with bronchial asthma and chronic obstructive pulmonary disease, Egyptian Journal of Bronchology 12 (2018) 20–26,.
[32]
S.-B. Zhang, M. Hong, X.-Y. Sun, D.W. Huang, D.-H. He, Y.-F. Chen, Y. Yuan, Y.-Q. Liu, Silybin has therapeutic efficacy against non-small cell lung cancer through targeting of skp2, Acta Materia Medica 1 (2022) 302–313,.
[33]
J.A. Sidey-Gibbons, C.J. Sidey-Gibbons, Machine learning in medicine: a practical introduction, BMC Med. Res. Methodol. 19 (2019) 1–18,.
[34]
J.M. Tracy, Y. Özkanca, D.C. Atkins, R.H. Ghomi, Investigating voice as a biomarker: deep phenotyping methods for early detection of Parkinson's disease, J. Biomed. Inf. 104 (2020),.
[35]
R. Dias, A. Torkamani, Artificial intelligence in clinical and genomic diagnostics, Genome Med. 11 (2019) 1–12,.
[36]
O. Ashraf, E. Rabold, K. Schlichtkrull, A. Singh, S. Venneti, M.M.D.A. Khan, R. Kulshreshtha, P.P. Naval, Voice-based screening and monitoring of chronic respiratory conditions, Chest 158 (2020) A1687,.
[37]
H. Lin, C. Karjadi, T.F. Ang, J. Prajakta, C. McManus, T.W. Alhanai, J. Glass, R. Au, Identification of digital voice biomarkers for cognitive health, Exploration of medicine 1 (2020) 406,.
[38]
L. Liu, Y. Li, S. Li, N. Hu, Y. He, R. Pong, D. Lin, L. Lu, M. Law, Comparison of next-generation sequencing systems, J. Biomed. Biotechnol. (2012) 2012,.
[39]
J. Amann, A. Blasimme, E. Vayena, D. Frey, V.I. Madai, P.Q. Consortium, Explainability for artificial intelligence in healthcare: a multidisciplinary perspective, BMC Med. Inf. Decis. Making 20 (2020) 1–9,.
[40]
H. Chen, C. Gomez, C.-M. Huang, M. Unberath, Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review, NPJ digital medicine 5 (2022) 156,.
[41]
C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell. 1 (2019) 206–215,.
[42]
C. Combi, B. Amico, R. Bellazzi, A. Holzinger, J.H. Moore, M. Zitnik, J.H. Holmes, A manifesto on explainability for artificial intelligence in medicine, Artif. Intell. Med. 133 (2022),.
[43]
P. Gupta, H. Wen, L. Di Francesco, F. Ayazi, Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders, Sci. Rep. 11 (2021),.
[44]
M. Gilfillan, A. Bhandari, V. Bhandari, Diagnosis and management of bronchopulmonary dysplasia, Bmj 375 (2021),.
[45]
S. Kaur, E. Larsen, J. Harper, B. Purandare, A. Uluer, M.A. Hasdianda, N.A. Umale, J. Killeen, E. Castillo, S. Jariwala, Development and validation of a respiratory-Responsive vocal biomarker–based tool for generalizable detection of respiratory impairment: independent case-control studies in multiple respiratory conditions including asthma, chronic obstructive pulmonary disease, and COVID-19, J. Med. Internet Res. 25 (2023),.
[46]
K. Wieczorek, S. Ananth, D. Valazquez-Pimentel, Acoustic biomarkers in asthma: a systematic review, J. Asthma (2024) 1–18,.
[47]
M.Z. Alam, A. Simonetti, R. Brillantino, N. Tayler, C. Grainge, P. Siribaddana, S.R. Nouraei, J. Batchelor, M.S. Rahman, E.V. Mancuzo, Predicting pulmonary function from the analysis of voice: a machine learning approach, Frontiers in digital health 4 (2022),.
[48]
Z. Zhu, S. Zhao, J. Li, Y. Wang, L. Xu, Y. Jia, Z. Li, W. Li, G. Chen, X. Wu, Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease, Respir. Res. 25 (2024) 1–12,.
[49]
A. Srivastava, S. Jain, R. Miranda, S. Patil, S. Pandya, K. Kotecha, Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Computer Science 7 (2021) e369,.
[50]
I. Tessler, A. Primov-Fever, S. Soffer, R. Anteby, N.A. Gecel, N. Livneh, E.E. Alon, E. Zimlichman, E. Klang, Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review, Eur. Arch. Oto-Rhino-Laryngol. 281 (2024) 863–871,.
[51]
S.-Y. Jung, C.-H. Liao, Y.-S. Wu, S.-M. Yuan, C.-T. Sun, Efficiently classifying lung sounds through depthwise separable CNN models with fused STFT and MFCC features, Diagnostics 11 (2021) 732,.
[52]
M. Phillips, T.L. Bauer, H.I. Pass, A volatile biomarker in breath predicts lung cancer and pulmonary nodules, J. Breath Res. 13 (2019),.
[53]
A.H. Sabry, O.I.D. Bashi, N.N. Ali, Y.M. Al Kubaisi, Lung disease recognition methods using audio-based analysis with machine learning, beliyon (2024),.
[54]
D.J. DePianto, S. Chandriani, A.R. Abbas, G. Jia, E.N. N'Diaye, P. Caplazi, S.E. Kauder, S. Biswas, S.K. Karnik, C. Ha, Heterogeneous gene expression signatures correspond to distinct lung pathologies and biomarkers of disease severity in idiopathic pulmonary fibrosis, Thorax 70 (2015) 48–56,.
[55]
Y. Kumai, Pathophysiology of fibrosis in the vocal fold: current research, future treatment strategies, and obstacles to restoring vocal fold pliability, Int. J. Mol. Sci. 20 (2019) 2551,.
[56]
N.M. Ralbovsky, I.K. Lednev, Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning, Chem. Soc. Rev. 49 (2020) 7428–7453,.
[57]
I. Minopoulou, A. Kleyer, M. Yalcin-Mutlu, F. Fagni, S. Kemenes, C. Schmidkonz, A. Atzinger, M. Pachowsky, K. Engel, L. Folle, Imaging in inflammatory arthritis: progress towards precision medicine, Nat. Rev. Rheumatol. 19 (2023) 650–665,.
[58]
S. Umirzakova, S. Ahmad, L.U. Khan, T. Whangbo, Medical image Super-Resolution for Smart healthcare applications: a comprehensive survey, Inf. Fusion (2023),.
[59]
E. Kallvik, J. Savolainen, S. Simberg, Vocal symptoms and voice quality in children with allergy and asthma, J. Voice 31 (2017) 515. e519–e515. e514,.
[60]
H.E. Hassen, A.M.A. Hasseba, Voice evaluation in asthma patients using inhaled corticosteroids, The Turkish Journal of Ear Nose and Throat 26 (2016) 101–108,.
[61]
M.M.D.A. Khan, P.P. Naval, R. Kulshreshtha, S. Venneti, A. Singh, VOICE-BASED monitoring OF COPD, Chest 160 (2021) A2173–A2174,. PlumX Metrics.
[62]
G.d.A.P. da Silva, T.D. Feltrin, F. dos Santos Pichini, C.A. Cielo, A.S. Pasqualoto, Quality of life predictors in voice of individuals with chronic obstructive pulmonary disease, J. Voice (2022),.
[63]
A.J. Hoffman, R.A. Brintnall, A. von Eye, J. Cooper, J.K. Brown, The voice of postsurgical lung cancer patients regarding supportive care needs, Lung Cancer Targets Ther. (2014) 21–31,.
[64]
C.F. Lee, P.N. Carding, M. Fletcher, The nature and severity of voice disorders in lung cancer patients, Logopedics Phoniatrics Vocology 33 (2008) 93–103,.
[65]
R.J. Davis, B. Messing, N.M. Cohen, L.M. Akst, Voice quality and laryngeal findings in patients with suspected lung cancer, Otolaryngology-Head Neck Surg. (Tokyo) 166 (2022) 133–138,.
[66]
Y. Wakwaya, D. Ramdurai, J.J. Swigris, Managing cough in idiopathic pulmonary fibrosis, Chest 160 (2021) 1774–1782,.
[67]
B.M. Lourenço, K.M. Costa, M. da Silva Filho, Voice disorder in cystic fibrosis patients, PLoS One 9 (2014),.
[68]
M. Desjardins, H.S. Bonilha, The impact of respiratory exercises on voice outcomes: a systematic review of the literature, J. Voice 34 (2020) 648. e641–e648. e639,.
[69]
D. Sánchez Morillo, A. Leon Jimenez, S.A. Moreno, Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease, J. Am. Med. Inf. Assoc. 20 (2013) e111–e117,.
[70]
A. Rossi, B. Butorac-Petanjek, M. Chilosi, B.G. Cosio, M. Flezar, N. Koulouris, J. Marin, N. Miculinic, G. Polese, M. Samaržija, Chronic obstructive pulmonary disease with mild airflow limitation: current knowledge and proposal for future research–a consensus document from six scientific societies, Int. J. Chronic Obstr. Pulm. Dis. (2017) 2593–2610,.
[71]
A. Higham, A.M. Quinn, J.E.D. Cançado, D. Singh, The pathology of small airways disease in COPD: historical aspects and future directions, Respiratory research 20 (2019) 1–11,.
[72]
R.L. Jones, P.B. Noble, J.G. Elliot, A.L. James, Airway remodelling in COPD: It's not asthma, Respirology 21 (2016) 1347–1356,.
[73]
S. Huang, M.M. Vasquez, M. Halonen, F.D. Martinez, S. Guerra, Asthma, airflow limitation and mortality risk in the general population, Eur. Respir. J. 45 (2015) 338–346,.
[74]
M. Phillips, N. Altorki, J.H. Austin, R.B. Cameron, R.N. Cataneo, R. Kloss, R.A. Maxfield, M.I. Munawar, H.I. Pass, A. Rashid, Detection of lung cancer using weighted digital analysis of breath biomarkers, Clinica chimica acta 393 (2008) 76–84,.
[75]
D.J. DePianto, S. Chandriani, A.R. Abbas, G. Jia, E.N. N'Diaye, P. Caplazi, S.E. Kauder, S. Biswas, S.K. Karnik, C. Ha, Heterogeneous gene expression signatures correspond to distinct lung pathologies and biomarkers of disease severity in idiopathic pulmonary fibrosis, Thorax (2014),. thoraxjnl-2013-204596.
[76]
L.M. Cross, M.A. Matthay, Biomarkers in acute lung injury: insights into the pathogenesis of acute lung injury, Crit. Care Clin. 27 (2011) 355–377,.
[77]
K.K. Lella, A. Pja, Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath, Alex. Eng. J. 61 (2022) 1319–1334,.
[78]
C.T. Engineer, C.A. Perez, Y.H. Chen, R.S. Carraway, A.C. Reed, J.A. Shetake, V. Jakkamsetti, K.Q. Chang, M.P. Kilgard, Cortical activity patterns predict speech discrimination ability, Nat. Neurosci. 11 (2008) 603–608,.
[79]
A. Yellamsetty, G.M. Bidelman, Brainstem correlates of concurrent speech identification in adverse listening conditions, Brain Res. 1714 (2019) 182–192,.
[80]
S. Krishnan, Y. Athavale, Trends in biomedical signal feature extraction, Biomed. Signal Process Control 43 (2018) 41–63,.
[81]
G. Sharma, K. Umapathy, S. Krishnan, Trends in audio signal feature extraction methods, Appl. Acoust. 158 (2020),.
[82]
P. Harar, J.B. Alonso-Hernandezy, J. Mekyska, Z. Galaz, R. Burget, Z. Smekal, Voice pathology detection using deep learning: a preliminary study, in: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), IEEE, 2017, pp. 1–4,.
[83]
N.Q. Abdulmajeed, B. Al‐Khateeb, M.A. Mohammed, Voice pathology identification system using a deep learning approach based on unique feature selection sets, Expet Syst. (2023),.
[84]
R. Jahangir, Y.W. Teh, H.F. Nweke, G. Mujtaba, M.A. Al-Garadi, I. Ali, Speaker identification through artificial intelligence techniques: a comprehensive review and research challenges, Expert Syst. Appl. 171 (2021),.
[85]
R. Jahangir, Y.W. Teh, F. Hanif, G. Mujtaba, Deep learning approaches for speech emotion recognition: state of the art and research challenges, Multimed. Tool. Appl. (2021) 1–68,.
[86]
Roy, N.; Barkmeier-Kraemer, J.; Eadie, T.; Sivasankar, M.P.; Mehta, D.; Paul, D.; Hillman, R. (2013): Evidence-based clinical voice assessment: a systematic review. https://doi.org/10.1044/1058-0360(2012/12-0014.
[87]
L. Hansen, Y.P. Zhang, D. Wolf, K. Sechidis, N. Ladegaard, R. Fusaroli, A generalizable speech emotion recognition model reveals depression and remission, Acta Psychiatr. Scand. 145 (2022) 186–199,.
[88]
N. Prodi, C. Visentin, A slight increase in reverberation time in the classroom affects performance and behavioral listening effort, Ear Hear. 43 (2022) 460–476,.
[89]
N. Soda, B.H. Rehm, P. Sonar, N.-T. Nguyen, M.J. Shiddiky, Advanced liquid biopsy technologies for circulating biomarker detection, J. Mater. Chem. B 7 (2019) 6670–6704,.
[90]
B.M. Klinkhammer, T. Lammers, F.M. Mottaghy, F. Kiessling, J. Floege, P. Boor, Non-invasive molecular imaging of kidney diseases, Nat. Rev. Nephrol. 17 (2021) 688–703,.
[91]
P. Cao, Q. Zhang, S. Wu, M.A. Sullivan, Y. Huang, W. Gong, Y. Lv, X. Zhai, Y. Zhang, Baseline differences in metabolic profiles of patients with lung squamous cell carcinoma responding or not responding to treatment with nanoparticle albumin-bound paclitaxel (nab-paclitaxel), Acta Materia Medica 2 (2023) 347–356,.
[92]
S.A. Khanna, J.W. Nance, S.A. Suliman, Detection and monitoring of interstitial lung disease in patients with systemic sclerosis, Curr. Rheumatol. Rep. 24 (2022) 166–173,.
[93]
M.S. Wijsenbeek, C.C. Moor, K.A. Johannson, P.D. Jackson, Y.H. Khor, Y. Kondoh, S.K. Rajan, G.C. Tabaj, B.E. Varela, P. van der Wal, Home monitoring in interstitial lung diseases, Lancet Respir. Med. 11 (2023) 97–110,.
[94]
S.M. Swain, M. Nishino, L.H. Lancaster, B.T. Li, A.G. Nicholson, B.J. Bartholmai, J. Naidoo, E. Schumacher-Wulf, K. Shitara, J. Tsurutani, Multidisciplinary clinical guidance on trastuzumab deruxtecan (T-DXd)–related interstitial lung disease/pneumonitis—focus on proactive monitoring, diagnosis, and management, Cancer Treat Rev. 106 (2022),.
[95]
M. Little, P. Mcsharry, S. Roberts, D. Costello, I. Moroz, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection, Nature Precedings (2007) 1,. 1.
[96]
P. Gómez-Vilda, R. Fernández-Baillo, A. Nieto, F. Díaz, F.J. Fernández-Camacho, V. Rodellar, A. Álvarez, R. Martínez, Evaluation of voice pathology based on the estimation of vocal fold biomechanical parameters, J. Voice 21 (2007) 450–476,.
[97]
A.A.S. Shaikh, M. Bhargavi, G.R. Naik, Unraveling the complexities of pathological voice through saliency analysis, Comput. Biol. Med. 166 (2023),.
[98]
R. Behroozmand, F. Almasganj, Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients' speech signal with unilateral vocal fold paralysis, Comput. Biol. Med. 37 (2007) 474–485,.
[99]
A. Rao, E. Huynh, T.J. Royston, A. Kornblith, S. Roy, Acoustic methods for pulmonary diagnosis, IEEE reviews in biomedical engineering 12 (2018) 221–239,.
[100]
M.Z. Alam, A. Simonetti, R. Brillantino, N. Tayler, C. Grainge, P. Siribaddana, S. Nouraei, J. Batchelor, M.S. Rahman, E.V. Mancuzo, Predicting pulmonary function from the analysis of voice: a machine learning approach, Frontiers in digital health 4 (2022),.
[101]
S. Xu, R.C. Deo, J. Soar, P.D. Barua, O. Faust, N. Homaira, A. Jaffe, A.L. Kabir, U.R. Acharya, Automated detection of airflow obstructive diseases: a systematic review of the last decade (2013-2022), Comput. Methods Progr. Biomed. (2023),.
[102]
R. Palaniappan, K. Sundaraj, N.U. Ahamed, Machine learning in lung sound analysis: a systematic review, Biocybern. Biomed. Eng. 33 (2013) 129–135,.
[103]
S. Gonem, W. Janssens, N. Das, M. Topalovic, Applications of artificial intelligence and machine learning in respiratory medicine, Thorax (2020),.
[104]
C. Poellabauer, N. Yadav, L. Daudet, S.L. Schneider, C. Busso, P.J. Flynn, Challenges in concussion detection using vocal acoustic biomarkers, IEEE Access 3 (2015) 1143–1160,.
[105]
K. Kaczmarek-Majer, G. Casalino, G. Castellano, M. Dominiak, O. Hryniewicz, O. Kamińska, G. Vessio, N. Díaz-Rodríguez, PLENARY: explaining black-box models in natural language through fuzzy linguistic summaries, Inform. Sciences. 614 (2022) 374–399,.
[106]
Y. Zhang, Y. Weng, J. Lund, Applications of explainable artificial intelligence in diagnosis and surgery, Diagnostics 12 (2022) 237,.
[107]
J. Laguarta, F. Hueto, B. Subirana, COVID-19 artificial intelligence diagnosis using only cough recordings, IEEE Open Journal of Engineering in Medicine and Biology 1 (2020) 275–281,.
[108]
A.A.A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S.J. Van Riel, M.M.W. Wille, M. Naqibullah, C.I. Sánchez, B. Van Ginneken, Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks, IEEE Trans. Med. Imaging 35 (2016) 1160–1169,.
[109]
Z. Zhang, E. Sejdić, Radiological images and machine learning: trends, perspectives, and prospects, Comput. Biol. Med. 108 (2019) 354–370,.
[110]
P. Lakhani, B. Sundaram, Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks, Radiology 284 (2017) 574–582,.
[111]
J. Ma, M.A. Guarnera, W. Zhou, H. Fang, F. Jiang, A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules, Translational oncology 10 (2017) 40–45,.
[112]
A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks, Nature 542 (2017) 115–118,.
[113]
M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, H. Larochelle, Brain tumor segmentation with deep neural networks, Med. Image Anal. 35 (2017) 18–31,.
[114]
G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A. Van Der Laak, B. Van Ginneken, C.I. Sánchez, A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017) 60–88,.
[115]
A. Christe, A.A. Peters, D. Drakopoulos, J.T. Heverhagen, T. Geiser, T. Stathopoulou, S. Christodoulidis, M. Anthimopoulos, S.G. Mougiakakou, L. Ebner, Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images, Invest. Radiol. 54 (2019) 627,.
[116]
M. Rana, M. Bhushan, Machine learning and deep learning approach for medical image analysis: diagnosis to detection, Multimed. Tool. Appl. 82 (2023) 26731–26769,.
[117]
V. Hassija, V. Chamola, A. Mahapatra, A. Singal, D. Goel, K. Huang, S. Scardapane, I. Spinelli, M. Mahmud, A. Hussain, Interpreting black-box models: a review on explainable artificial intelligence, Cognitive Computation (2023) 1–30,.
[118]
Z. Obermeyer, B. Powers, C. Vogeli, S. Mullainathan, Dissecting racial bias in an algorithm used to manage the health of populations, Science 366 (2019) 447–453,.
[119]
D.S. Char, N.H. Shah, D. Magnus, Implementing machine learning in health care—addressing ethical challenges, N. Engl. J. Med. 378 (2018) 981,.
[120]
D. Castelvecchi, Can we open the black box of AI?, Nature News 538 (2016) 20,.
[121]
T. Evans, C.O. Retzlaff, C. Geißler, M. Kargl, M. Plass, H. Müller, T.-R. Kiehl, N. Zerbe, A. Holzinger, The explainability paradox: challenges for xAI in digital pathology, Future Generat. Comput. Syst. 133 (2022) 281–296.
[122]
B.N. Patel, L. Rosenberg, G. Willcox, D. Baltaxe, M. Lyons, J. Irvin, P. Rajpurkar, T. Amrhein, R. Gupta, S. Halabi, Human–machine partnership with artificial intelligence for chest radiograph diagnosis, NPJ digital medicine 2 (2019) 111,.
[123]
S.I. Lambert, M. Madi, S. Sopka, A. Lenes, H. Stange, C.-P. Buszello, A. Stephan, An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals, NPJ Digital Medicine 6 (2023) 111.
[124]
E.J. Topol, High-performance medicine: the convergence of human and artificial intelligence, Nat. Med. 25 (2019) 44–56,.
[125]
S. Benjamens, P. Dhunnoo, B. Meskó, The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database, NPJ digital medicine 3 (2020) 118,.
[126]
M. Langer, D. Oster, T. Speith, H. Hermanns, L. Kästner, E. Schmidt, A. Sesing, K. Baum, What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research, Artif. Intell. 296 (2021),.
[127]
E.H. Shortliffe, M.J. Sepúlveda, Clinical decision support in the era of artificial intelligence, JAMA 320 (2018) 2199–2200,.
[128]
H. Alfalahi, S.B. Dias, A.H. Khandoker, K.R. Chaudhuri, L.J. Hadjileontiadis, A scoping review of neurodegenerative manifestations in explainable digital phenotyping, npj Parkinson's Disease 9 (2023) 49,.
[129]
A. Holzinger, C. Biemann, C.S. Pattichis, D.B. Kell, What do we need to build explainable AI systems for the medical domain?, arXiv preprint arXiv:1712.09923 (2017),.
[130]
E. Tjoa, C. Guan, A survey on explainable artificial intelligence (xai): toward medical xai, IEEE Transact. Neural Networks Learn. Syst. 32 (2020) 4793–4813,.
[131]
D.V. Carvalho, E.M. Pereira, J.S. Cardoso, Machine learning interpretability: a survey on methods and metrics, Electronics 8 (2019) 832,.
[132]
S. Ali, T. Abuhmed, S. El-Sappagh, K. Muhammad, J.M. Alonso-Moral, R. Confalonieri, R. Guidotti, J. Del Ser, N. Díaz-Rodríguez, F. Herrera, Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence, Inf. Fusion 99 (2023),.
[133]
A. Adadi, M. Berrada, Peeking inside the black-box: a survey on explainable artificial intelligence (XAI), IEEE Access 6 (2018) 52138–52160,.
[134]
A.F. Markus, J.A. Kors, P.R. Rijnbeek, The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies, J. Biomed. Inf. 113 (2021),.
[135]
W. Samek, K.-R. Müller, Towards explainable artificial intelligence, in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, pp. 5–22,.
[136]
C.M. Cutillo, K.R. Sharma, L. Foschini, S. Kundu, M. Mackintosh, K.D. Mandl, M.i.H.W.W.G.B.T.C.E.C.C.G.K.G.V.J.R.S.B.S.N. 1, Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency, NPJ digital medicine 3 (2020) 47,.
[137]
T. Chen, C. Shang, P. Su, E. Keravnou-Papailiou, Y. Zhao, G. Antoniou, Q. Shen, A decision tree-initialised neuro-fuzzy approach for clinical decision support, Artif. Intell. Med. 111 (2021),.
[138]
S.M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst. 30 (2017),.
[139]
M.T. Ribeiro, S. Singh, C. Guestrin, Why should i trust you?" Explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144,.
[140]
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, Causability and explainability of artificial intelligence in medicine, Wiley Interdisciplinary Reviews: Data Min. Knowl. Discov. 9 (2019),.
[141]
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi, A survey of methods for explaining black box models, ACM Comput. Surv. 51 (2018) 1–42,.
[142]
B. Alsinglawi, O. Alshari, M. Alorjani, O. Mubin, F. Alnajjar, M. Novoa, O. Darwish, An explainable machine learning framework for lung cancer hospital length of stay prediction, Sci. Rep. 12 (2022) 607,.
[143]
A. Rafferty, R. Ramaesh, A. Rajan, Transparent and clinically interpretable AI for lung cancer detection in chest X-rays, arXiv preprint arXiv:2403.19444 (2024),.
[144]
S. Narteni, I. Baiardini, F. Braido, M. Mongelli, Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients, PLoS One 19 (2024),.
[145]
T. Bashford, M.H.S. Lau, M.M. Huntly, M.N. Morgan, M.A. Iyenoma, T. Powell, B. Zeng, W. Treforest, AI classification of respiratory illness through vocal biomarkers and a bespoke articulatory speech protocol, Int. J. Simulat. Syst. Sci. Technol. 25 (2024),.
[146]
N.S. Haider, A. Behera, Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybern. Biomed. Eng. 42 (2022) 42–59,.
[147]
A. Idrisoglu, Voice for decision support in healthcare applied to chronic obstructive pulmonary disease classification: a machine learning approach, Blekinge Tekniska Högskola (2024).
[148]
S.O. Ooko, D. Mukanyiligira, J.P. Munyampundu, J. Nsenga, Synthetic Exhaled breath data-based Edge AI model for the prediction of chronic obstructive pulmonary disease, in: 2021 International Conference on Computing and Communications Applications and Technologies (I3CAT), IEEE, 2021, pp. 1–6,.
[149]
V. Nathan, K. Vatanparvar, M.M. Rahman, E. Nemati, J. Kuang, Assessment of chronic pulmonary disease patients using biomarkers from natural speech recorded by mobile devices, in: 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), IEEE, 2019, pp. 1–4,.
[150]
V. Nathan, M.M. Rahman, K. Vatanparvar, E. Nemati, E. Blackstock, J. Kuang, Extraction of voice parameters from continuous running speech for pulmonary disease monitoring, in: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2019, pp. 859–864,.
[151]
L. Dai, X. Yang, H. Li, X. Zhao, L. Lin, Y. Jiang, Y. Wang, Z. Li, H. Shen, A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia, Artif. Intell. Med. 149 (2024),.
[152]
J.D.S. Sara, D. Orbelo, E. Maor, L.O. Lerman, A. Lerman, Guess what We can Hear–novel voice biomarkers for the remote detection of disease, Mayo Clin. Proc. (2023),. Elsevier.
[153]
J.D.S. Sara, E. Maor, B. Borlaug, B.R. Lewis, D. Orbelo, L.O. Lerman, A. Lerman, Non-invasive vocal biomarker is associated with pulmonary hypertension, PLoS One 15 (2020),.
[154]
R. Groh, Z. Lei, L. Martignetti, N.Y. Li-Jessen, A.M. Kist, Efficient and explainable deep neural networks for airway symptom detection in support of wearable health technology, Advanced Intelligent Systems 4 (2022),.
[155]
N. Sasikumar, M. Senthilkumar, Deep convolutional generative adversarial networks for automated segmentation and detection of lung adenocarcinoma using red deer optimization algorithm, Inf. Technol. Control 52 (2023) 680–692,.
[156]
J.S. Jennifer, T.S. Sharmila, A neutrosophic set approach on chest X-rays for automatic lung infection detection, Inf. Technol. Control 52 (2023) 37–52,.
[157]
N.-u. Rehman, M.S. Zia, T. Meraj, H.T. Rauf, R. Damaševičius, A.M. El-Sherbeeny, M.A. El-Meligy, A self-activated cnn approach for multi-class chest-related COVID-19 detection, Appl. Sci. 11 (2021) 9023,.
[158]
A. Jaszcz, D. Połap, R. Damaševičius, Lung x-ray image segmentation using heuristic red fox optimization algorithm, Sci. Program. 2022 (2022) 1–8,.
[159]
T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 (2006) 861–874,.
[160]
Z. Obermeyer, E.J. Emanuel, Predicting the future—big data, machine learning, and clinical medicine, N. Engl. J. Med. 375 (2016) 1216,.
[161]
Z. Naz, M.U.G. Khan, T. Saba, A. Rehman, H. Nobanee, S.A. Bahaj, An explainable AI-enabled framework for interpreting pulmonary diseases from chest radiographs, Cancers 15 (2023) 314,.
[162]
N. Das, S. Happaerts, I. Gyselinck, M. Staes, E. Derom, G. Brusselle, F. Burgos, M. Contoli, A.T. Dinh-Xuan, F.M. Franssen, Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation, Eur. Respir. J. 61 (2023),.
[163]
F. Doshi-Velez, B. Kim, Towards a rigorous science of interpretable machine learning, arXiv preprint arXiv:1702.08608 (2017),.
[164]
V. Pitroda, M.M. Fouda, Z.M. Fadlullah, An explainable AI model for interpretable lung disease classification, in: 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), IEEE, 2021, pp. 98–103,.
[165]
N.A. Wani, R. Kumar, J. Bedi, DeepXplainer: an interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence, Comput. Methods Progr. Biomed. 243 (2024),.
[166]
O. Wysocki, J.K. Davies, M. Vigo, A.C. Armstrong, D. Landers, R. Lee, A. Freitas, Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making, Artif. Intell. 316 (2023),.
[167]
A. Glick, M. Clayton, N. Angelov, J. Chang, Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians, JAMIA open 5 (2022),. ooac031.
[168]
Rane, N.; Choudhary, S.; Rane, J. (2023): Explainable artificial intelligence (XAI) in healthcare: interpretable models for clinical decision support. Available at: SSRN 4637897 https://doi.org/10.2139/ssrn.4637897.
[169]
S. Liu, A.B. McCoy, J.F. Peterson, T.A. Lasko, D.F. Sittig, S.D. Nelson, J. Andrews, L. Patterson, C.M. Cobb, D. Mulherin, Leveraging explainable artificial intelligence to optimize clinical decision support, J. Am. Med. Inf. Assoc. (2024),. ocae019.
[170]
J. Yang, A.A. Soltan, D.A. Clifton, Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening, NPJ digital medicine 5 (2022) 69,.
[171]
C. Meske, E. Bunde, J. Schneider, M. Gersch, Explainable artificial intelligence: objectives, stakeholders, and future research opportunities, Inf. Syst. Manag. 39 (2022) 53–63,.
[172]
F. Maleki, K. Ovens, R. Gupta, C. Reinhold, A. Spatz, R. Forghani, Generalizability of machine learning models: Quantitative evaluation of three methodological pitfalls, Radiology, Artif. Intell. 5 (2022),.
[173]
S.C.H. Yang, T. Folke, P. Shafto, Abstraction, validation, and generalization for explainable artificial intelligence, Applied AI Letters 2 (2021) e37,.
[174]
S. Khan, S. Tsutsumi, T. Yairi, S. Nakasuka, Robustness of AI-based prognostic and systems health management, Annu. Rev. Control 51 (2021) 130–152,.
[175]
N. Feldkamp, S. Strassburger, From explainable AI to explainable simulation: using machine learning and XAI to understand system robustness, in: Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 2023, pp. 96–106,.
[176]
L.H. Gilpin, D. Bau, B.Z. Yuan, A. Bajwa, M. Specter, L. Kagal, Explaining explanations: an overview of interpretability of machine learning, in: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2018, pp. 80–89,.
[177]
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, N. Elhadad, Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1721–1730,.
[178]
J. Wiens, E.S. Shenoy, Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology, Clin. Infect. Dis. 66 (2018) 149–153,.
[179]
A.L. Beam, I.S. Kohane, Big data and machine learning in health care, JAMA 319 (2018) 1317–1318,.
[180]
A. Rajkomar, E. Oren, K. Chen, A.M. Dai, N. Hajaj, M. Hardt, P.J. Liu, X. Liu, J. Marcus, M. Sun, Scalable and accurate deep learning with electronic health records, NPJ digital medicine 1 (2018) 18,.
[181]
S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng. 22 (2009) 1345–1359,.
[182]
K.M. Boehm, P. Khosravi, R. Vanguri, J. Gao, S.P. Shah, Harnessing multimodal data integration to advance precision oncology, Nat. Rev. Cancer 22 (2022) 114–126,.
[183]
A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, J. Dean, A guide to deep learning in healthcare, Nature medicine 25 (2019) 24–29,.
[184]
V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA 316 (2016) 2402–2410,.
[185]
H. Guan, M. Liu, Domain adaptation for medical image analysis: a survey, IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. 69 (2021) 1173–1185,.
[186]
M. Talo, U.B. Baloglu, Ö. Yıldırım, U.R. Acharya, Application of deep transfer learning for automated brain abnormality classification using MR images, Cognit. Syst. Res. 54 (2019) 176–188,.
[187]
T. Shaikhina, N.A. Khovanova, Handling limited datasets with neural networks in medical applications: a small-data approach, Artif. Intell. Med. 75 (2017) 51–63,.
[188]
E. Vayena, A. Blasimme, I.G. Cohen, Machine learning in medicine: addressing ethical challenges, PLoS Med. 15 (2018),.
[189]
A. Rajkomar, M. Hardt, M.D. Howell, G. Corrado, M.H. Chin, Ensuring fairness in machine learning to advance health equity, Ann. Intern. Med. 169 (2018) 866–872,.
[190]
D. de la Iglesia, M. García-Remesal, A. Anguita, M. Muñoz-Mármol, C. Kulikowski, V. Maojo, A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from clinicaltrials. gov, PLoS One 9 (2014),.
[191]
W.N. Price, I.G. Cohen, Privacy in the age of medical big data, Nat. Med. 25 (2019) 37–43,.
[192]
S. Gerke, T. Minssen, G. Cohen, Ethical and legal challenges of artificial intelligence-driven healthcare, in: Artificial Intelligence in Healthcare, Elsevier, 2020, pp. 295–336.
[193]
S. Wachter, B. Mittelstadt, C. Russell, Counterfactual explanations without opening the black box: automated decisions and the GDPR, Harv. JL & Tech. 31 (2017) 841,.
[194]
A. Gatouillat, Y. Badr, B. Massot, E. Sejdić, Internet of medical things: a review of recent contributions dealing with cyber-physical systems in medicine, IEEE Internet Things J. 5 (2018) 3810–3822,.
[195]
R. Miotto, F. Wang, S. Wang, X. Jiang, J.T. Dudley, Deep learning for healthcare: review, opportunities and challenges, Brief. Bioinform. 19 (2018) 1236–1246,.
[196]
T. Shaik, X. Tao, N. Higgins, L. Li, R. Gururajan, X. Zhou, U.R. Acharya, Remote patient monitoring using artificial intelligence: current state, applications, and challenges, Wiley Interdisciplinary Reviews: Data Min. Knowl. Discov. 13 (2023),.
[197]
F. Lei, Cross-cultural Adaptation and Validation of Lung Cancer Screening Health Belief Scale, University of California, Los Angeles, 2022.
[198]
H.-C. Thorsen-Meyer, A.B. Nielsen, A.P. Nielsen, B.S. Kaas-Hansen, P. Toft, J. Schierbeck, T. Strøm, P.J. Chmura, M. Heimann, L. Dybdahl, Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records, The Lancet Digital Health 2 (2020) e179–e191,.
[199]
S.M. Lauritsen, M. Kristensen, M.V. Olsen, M.S. Larsen, K.M. Lauritsen, M.J. Jørgensen, J. Lange, B. Thiesson, Explainable artificial intelligence model to predict acute critical illness from electronic health records, Nat. Commun. 11 (2020) 3852,.
[200]
G. Mancioppi, E. Rovini, L. Fiorini, R. Zeghari, A. Gros, V. Manera, P. Robert, F. Cavallo, Mild cognitive impairment identification based on motor and cognitive dual-task pooled indices, PLoS One 18 (2023),.
[201]
N. Pfeuffer, L. Baum, W. Stammer, B.M. Abdel-Karim, P. Schramowski, A.M. Bucher, C. Hügel, G. Rohde, K. Kersting, O. Hinz, Explanatory interactive machine learning: establishing an action design research process for machine learning projects, Business & Information Systems Engineering 65 (2023) 677–701,.
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