References
- Raja, S. N., D. B. Carr, M. Cohen et al. The Revised International Association for the Study of Pain Definition of Pain: Concepts, Challenges, and Compromises. – Pain, Vol. 161, 2020, No 9, pp. 1976-1982.
- Bhatt, R. R. Decoding the Neurological and Genetic Underpinnings of Chronic Pain. University of Southern California, 2024.
- El-Tallawy, S. N., J. V. Pergolizzi, I. Vasiliu-Feltes, R. S. Ahmed, J. K. LeQuang, H. N. El-Tallawy, G. Varrassi, M. S. Nagiub. Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review. – Pain and Therapy, 2024, pp. 1-25.
- Løkeland, L. S., F. Guribye, A. Stensvold, I. Grindheim. Tangible Interactions for Pain Assessment in Palliative Care. – In: Proc. of 13th Nordic Conference on Human-Computer Interaction, 2024, pp. 1-15.
- Ben Aoun, N. A Review of Automatic Pain Assessment from Facial Information Using Machine Learning. – Technologies, Vol. 12, 2024, No 6, 92.
- Hadjiat, Y., L. Arendt-Nielsen. Digital Health in Pain Assessment, Diagnosis, and Management: Overview and Perspectives. – Frontiers in Pain Research, Vol. 4, 2023, 1097379.
- Murala, D. K., S. K. Panda, S. P. Dash. MedMetaverse: Medical Care of Chronic Disease Patients and Managing Data Using Artificial Intelligence, Blockchain, and Wearable Devices, State-of-the-Art Methodology. – IEEE Access, Vol. 11, 2023, pp. 138954-138985.
- Fontaine, D., V. Vielzeuf, P. Genestier, P. Limeux, S. Santucci-Sivilotto, E. Mory, N. Darmon, M. Lanteri-Minet, M. Mokhtar, M. Laine, D. Vistoli. Artificial Intelligence to Evaluate Postoperative Pain Based on Facial Expression Recognition. – European Journal of Pain, Vol. 26, 2022, No 6, pp. 1282-1291.
- Barua, P. D., N. Baygin, S. Dogan, M. Baygin, N. Arunkumar, H. Fujita, T. Tuncer, R. S. Tan, E. Palmer, M. M. Azizan, N. A. Kadri. Automated Detection of Pain Levels Using Deep Feature Extraction from Shutter Blinds-Based, Dynamically Sized Horizontal Patches with Facial Images. – Scientific Reports, Vol. 12, 2022, No 1, 17297.
- Kelati, A., E. Nigussie, I. B. Dhaou, J. Plosila, H. Tenhunen. Real-Time Classification of Pain Level Using Zygomaticus and Corrugator EMG Features. – Electronics, Vol. 11, 11 January 2022, 1671.
- Benavent-Lledo, M., D. Mulero-Pérez, D. Ortiz-Pérez, J. Rodríguez-Juan, A. Berenguer-Agullo, A. Psarrou, J. García-Rodríguez. A Comprehensive Study on Pain Assessment from Multimodal Sensor Data. – Semsors, Vol. 23, 2023, No 24, 9675.
- Fang, J., W. Wu, J. Liu, S. Zhang. Deep Learning-Guided Postoperative Pain Assessment in Children. – Pain, Vol. 164, 2023, No 9, pp. 2029-2035.
- Ghosh, A., S. Umer, M. K. Khan, R. K. Rout, B. C. Dhara. Smart Sentiment Analysis System for Pain Detection Using Cutting-Edge Techniques in a Smart Healthcare Framework. – Cluster Computing, Vol. 26, 2023, No 1, pp. 119-135.
- Safavi, F., K. Patel, R. K. Vinjamuri. Towards Efficient Deep Learning Models for Facial Expression Recognition Using Transformers. – In: Proc. of 19th IEEE International Conference on Body Sensor Networks (BSN’23), IEEE, 9 October 2023, pp. 1-4.
- Mendu, S., S. L. Doyle Fosco, S. T. Lanza, S. Abdullah. Designing Voice Interfaces to Support Mindfulness-Based Pain Management. – Digital Health, Vol. 9, 2023, pp. 1-15.
- Lu, Z., B. Ozek, S. Kamarthi. Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals. – Frontiers in Physiology, Vol. 14, 2023, 1294577.
- Sabater-Gárriz, Á., F. X. Gaya-Morey, J. M. Buades-Rubio, C. Manresa-Yee, P. Montoya, I. Riquelme. Automated Facial Recognition System Using Deep Learning for Pain Assessment in Adults with Cerebral Palsy. – Digital Health, Vol. 10, 2024, pp. 1-22.
- Zheng, J., Y. Lin. Using Physiological Signals for Pain Assessment: An Evaluation of Deep Learning Models. – In: Proc. of 30th IEEE International Conference on Mechatronics and Machine Vision in Practice (M2VIP’24), 3 October 2024, pp. 1-6.
- Hausmann, J., M. S. Salekin, G. Zamzmi, P. R. Mouton, S. Prescott, T. Ho, Y. Sun, D. Goldgof. Accurate Neonatal Face Detection for Improved Pain Classification in the Challenging NICU Setting. – IEEE Access, Vol. 1, 1 April 2024.
- Kristian, Y., N. Simogiarto, M. T. Sampurna, E. Hanindito. Ensemble of Multimodal Deep Learning Autoencoder for Infant Cry and Pain Detection. Version 1; Peer Review: 1 Approved, 2022.
- Sandeep, P. V., N. S. Kumar. Pain Detection through Facial Expressions in Children with Autism Using Deep Learning. – Soft Computing, Vol. 28, March 2024, No 5, pp. 4621-4630.
- Talaat, F. M., Z. H. Ali, R. R. Mostafa, N. El-Rashidy. Real-Time Facial Emotion Recognition Model Based on Kernel Autoencoder and Convolutional Neural Network for Autism Children. – Soft Computing, Vol. 28, May 2024, No 9, pp. 6695-6708.
- Aliradi, R., N. Chenni, M. Touami. Estimation for Pain from Facial Expression Based on XQEDA and Deep Learning. – International Journal of Information Technology, Vol. 17, January 2025, No 1, pp. 655-663.
- Abdallah, I. B., Y. Bouteraa. An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach. – Sensors, Vol. 24, 6 Feb 2024 No 4, 1047.
- Wahab Sait, A. R., A. K. Dutta. Developing a Pain Identification Model Using a Deep Learning Technique. – Journal of Disability Research, Vol. 3, 4 April 2024, No 3, 20240028.
- Gutierrez, R., J. Garcia-Ortiz, W. Villegas-Ch. Multimodal AI Techniques for Pain Detection: Integrating Facial Gesture and Paralanguage Analysis. – Frontiers in Computer Science, Vol. 6, 29 July 2024, 1424935.
- Dáad, A., W. Aljebreen, D. M. Ibrahim. PainMeter: Automatic Assessment of Pain Intensity Levels from Multiple Physiological Signals Using Machine Learning. – IEEE Access, Vol. 12, 2024, pp. 48349-48365.
- https://github.com/philippwerner/pain-database-list?tab=readme-ov-file#unbc-mcmaster-shoulder-pain-expression-archive-database
- Lucey, P., J. F. Cohn, K. M. Prkachin, P. E. Solomon, I. Matthews. Painful Data: The UNBC-McMaster Shoulder Pain Expression Archive Database. – In: Proc. of IEEE International Conference on Automatic Face & Gesture Recognition (FG’11), IEEE, 21 March 2011, pp. 57-64.
- Zhang, L., S. Walter, X. Ma, P. Werner, A. Al-Hamadi, H. C. Traue, S. Gruss. BioVid Emo DB: A Multimodal Database for Emotion Analyses Validated by Subjective Ratings. – In: Proc. of IEEE Symposium Series on Computational Intelligence (SSCI’16), IEEE, 6 December 2016, pp. 1-6.
- Zhang, X., L. Yin, J. F. Cohn, S. Canavan, M. Reale, A. Horowitz, P. Liu, J. M. Girard. Bp4d-Spontaneous: A High-Resolution Spontaneous 3D Dynamic Facial Expression Database. – Image and Vision Computing, Vol. 32, 2014, No 10, pp. 692-706.
- Zhang, Z., J. M. Girard, Y. Wu, X. Zhang, P. Liu, U. Ciftci, S. Canavan, M. Reale, A. Horowitz, H. Yang, J. F. Cohn. Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3438-3446.
- Brahnam, S., C. F. Chuang, F. Y. Shih, M. R. Slack. SVM Classification of Neonatal Facial Images of Pain. – In: I. Bloch, A. Petrosino, A. G. B. Tettamanzi, Eds. Fuzzy Logic and Applications. Lecture Notes in Computer Science. Vol. 384. Springer, Berlin, Heidelberg, 2005, pp. 111-115.
- Kolsoum, D., R. Froutan, A. Ebadi. Challenges Faced by Nurses in Using the Pain Assessment Scale in Patients Unable to Communicate: A Qualitative Study. – BMC Nursing, Vol. 17, 2018, pp. 1-8.
- Harrison, D., M. Sampson, J. Reszel, K. Abdulla, N. Barrowman, J. Cumber, A. Fuller, C. Li, S. Nicholls, C. M. Pound. Too Many Crying Babies: A Systematic Review of Pain Management Practices during Immunizations on YouTube. – BMC Pediatrics, Vol. 14, 2014, pp. 1-8.
- Mittal, Vinay Kumar. Discriminating the Infant Cry Sounds due to Pain vs Discomfort towards Assisted Clinical Diagnosis. – In: Proc. of 7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT’16), 2016.
- Berthouze, N., M. Valstar, A. Williams, J. Egede, T. Olugbade, C. Wang, H. Meng, M. Aung, N. Lane, S. Song. Emopain Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions. – arXiv. 2020 Jan.arXiv-2001.
- Aung, M. S., S. Kaltwang, B. Romera-Paredes, B. Martinez, A. Singh, M. Cella, M. Valstar, H. Meng, A. Kemp, M. Shafizadeh, A. C. Elkins. The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges, and the Multimodal EmoPain Dataset. – IEEE Transactions on Affective Computing, Vol. 7, 2015, No 4, pp. 435-351.
- Velana, M., S. Gruss, G. Layher, P. Thiam, Y. Zhang, D. Schork, V. Kessler, S. Meudt, H. Neumann, J. Kim, F. Schwenker. The Senseemotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Pain-and Emotion-Recognition System. – In: Proc. of 4th IAPR TC 9 Workshop, Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS’16), Cancun, Mexico, 4 December 2016, Springer International Publishing, Revised Selected Papers, Vol. 4, 2017, pp. 127-139).
- Gruss, S., M. Geiger, P. Werner, O. Wilhelm, H. C. Traue, A. Al-Hamadi, S. Walter. Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli. 2016.
- Darnall, B. D., A. Roy, A. L. Chen, M. S. Ziadni, R. T. Keane, D. S. You, K. Slater, H. Poupore-King, I. Mackey, M. C. Kao, K. F. Cook. Comparison of a Single-Session Pain Management Skills Intervention with a Single-Session Health Education Intervention and 8 Sessions of Cognitive Behavioral Therapy in Adults with Chronic Low Back Pain: A Randomized Clinical Trial. – JAMA Network Open, Open, Vol. 4, 2 August 2021, No 8, pp. 1-16.
- Werner, P., D. Lopez-Martinez, S. Walter, A. Al-Hamadi, S. Gruss, R. W. Picard. Automatic Recognition Methods Supporting Pain Assessment: A Survey. – IEEE Transactions on Affective Computing, Vol. 13, 14 October 2019, No 1, 53052.
- Haque, M. A., R. B. Bautista, F. Noroozi, K. Kulkarni, C. B. Laursen, R. Irani, M. Bellantonio, S. Escalera, G. Anbarjafari, K. Nasrollahi, O. K. Andersen. Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities. – In: Proc. of 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG’18), IEEE, 15 May 2018, pp. 250-257.
- Cascella, M., F. Monaco, O. Piazza. Artificial Intelligence and Pain Medicine: an Introduction. – Journal of Pain Research, 31 December 2024, pp. 1735-1736.
- Tian, Y. Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm. – IEEE Access, Vol. 8, 30 Juni 2020, pp. 125731-125744.
- Serraoui, I., E. Granger, A. Hadid, A. Taleb-Ahmed. Pain Analysis Using Adaptive Hierarchical Spatiotemporal Dynamic Imaging. – arXiv preprint arXiv 2312.06920. 12 December 2023.
- Fontaine, D., V. Vielzeuf, P. Genestier, P. Limeux, S. Santucci-Sivilotto, E. Mory, N. Darmon, M. Lanteri-Minet, M. Mokhtar, M. Laine, D. Vistoli. Artificial Intelligence to Evaluate Postoperative Pain Based on Facial Expression Recognition. – European Journal of Pain, Vol. 26, 2022, No 6, pp. 1282-1291.
- El-Tallawy, S. N., R. S. Ahmed, S. M. Shabi, F. Z. Al-Zabidi, A. R. Zaidi, G. Varrassi, J. V. Pergolizzi, J. A. LeQuang, A. Paladini, S. N. EL-Tallawy, F. Z. Al-Zabidi. The Challenges of Pain Assessment in Geriatric Patients with Dementia: A Review. – Cureus, Vol. 15, 2023, No 11.
- Gkikas, S., N. S. Tachos, S. Andreadis, V. C. Pezoulas, D. Zaridis, G. Gkois, A. Matonaki, T. G. Stavropoulos, D. I. Fotiadis. Multimodal Automatic Assessment of Acute Pain through Facial Videos and Heart Rate Signals Utilizing Transformer-Based Architectures. – Frontiers in Pain Research, Vol. 5, 2024, 1372814.
- Prkachin, K. M., Z. Hammal. Computer Mediated Automatic Detection of Pain-Related Behavior: Prospect, Progress, Perils. – Frontiers in Pain Research, Vol. 3, 2022, 849950.
- Fang, J., W. Wu, J. Liu, S. Zhang. Deep Learning-Guided Postoperative Pain Assessment in Children. – Pain, Vol. 164, 2023, No 9, pp. 2029-2035.
- Voytovich, L., C. Greenberg. Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete. – In: Machine Learning in Clinical Neuroscience: Foundations and Applications. Cham, Springer International Publishing, 2021, pp. 207-214.
- Cascella, M., D. Schiavo, A. Cuomo, A. Ottaiano, F. Perri, R. Patrone, S. Migliarelli, E. G. Bignami, A. Vittori, F. Cutugno. Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives. – Pain Research and Management, Vol. 2023, 2023, No 1, 6018736.
- Carlson, L. A., W. M. Hooten. Pain – Linguistics and Natural Language Processing. – Mayo Clinic Proceedings: Innovations, Quality & Outcomes, Vol. 4, 2020, No 3, pp. 346-347.
- Ghosh, A., B. C. Dhara, C. Pero, S. Umer. A Multimodal Sentiment Analysis System for Recognizing Person Aggressiveness in Pain Based on Textual and Visual Information. – Journal of Ambient Intelligence and Humanized Computing, Vol. 14, 2023, No 4, pp. 4489-4501.
- Moosaei, M., S. K. Das, D. O. Popa, L. D. Riek. Using Facially Expressive Robots to Calibrate Clinical Pain Perception. – In: Proc. of 2017 ACM/IEEE International Conference on Human-Robot Interaction, 6 March 2017, pp. 32-41.
- Dai, L., J. Broekens, K. P. Truong. Real-Time Pain Detection in Facial Expressions for Health Robotics. – In: Proc. of 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW’19), 3 September 2019, pp. 277-283.
- Higgins, A., A. Llewellyn, E. Dures, P. Caleb-Solly. Robotics Technology for Pain Treatment and Management: A Review. – In: Proc. of International Conference on Social Robotics, Cham, Springer Nature Switzerland, 13 Decemer 2022, pp. 534-545.
- Susam, B. T., N. T. Riek, M. Akcakaya, X. Xu, V. R. de Sa, H. Nezamfar, D. Diaz, K. D. Craig, M. S. Goodwin, J. S. Huang. Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning. – IEEE Transactions on Biomedical Engineering, Vol. 69, 2021, No 1, pp. 422-431.
- Fatemeh, P., S. Radhakrishnan, S. Kamarthi. Exploration of Physiological Sensors, Features, and Machine Learning Models for Pain Intensity Estimation. – Plos one, Vol. 16, 2021, No 7, e0254108.
- Sikka, K., A. A. Ahmed, D. Diaz, M. S. Goodwin, K. D. Craig, M. S. Bartlett, J. S. Huang. Automated Assessment of Children’s Postoperative Pain Using Computer Vision. – Pediatrics, Vol. 136, 2015, No 1, pp. e124-e131.
- Sabater-Gárriz, Á., J. Molina-Mula, P. Montoya, I. Riquelme. Pain Assessment Tools in Adults with Communication Disorders: Systematic Review and Meta-Analysis. – BMC Neurology, Vol. 24, 2024, No 1., 66.
- Pinzon-Arenas, J. O., Y. Kong, K. H. Chon, H. F. Posada-Quintero. Design and Evaluation of Deep Learning Models for Continuous Acute Pain Detection Based on Phasic Electrodermal Activity. – IEEE Journal of Biomedical and Health Informatics, Vol. 27, 2023, No 9, pp. 4250-4260.
- Kong, Y., H. F. Posada-Quintero, K. H. Chon. Pain Detection Using a Smartphone in Real Time. – In: Proc. of 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC’20), IEEE, 20 July 2020, pp. 4526-4529.
- Naeini, E. K., S. Shahhosseini, A. Subramanian, T. Yin, A. M. Rahmani, N. Dutt. An Edge-Assisted and Smart System for Real-Time Pain Monitoring. – In: Proc. of IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE’19), IEEE, 25 September 2019, pp. 47-52.
- Barreveld, A. M., M. L. Rosén Klement, S. Cheung, U. Axelsson, J. I. Basem, A. S. Reddy, C. A. Borrebaeck, N. Mehta. An Artificial Intelligence-Powered, Patient-Centric Digital Tool for Self-Management of Chronic Pain: A Prospective, Multicenter Clinical Trial. – Pain Medicine, Vol. 24, 2023, No 9, pp. 1100-1110.
- Nagireddi, J. N., A. K. Vyas, M. R. Sanapati, A. Soin, L. Manchikanti. The Analysis of Pain Research through the Lens of Artificial Intelligence and Machine Learning. – Pain Physician, Vol. 25, 2022, No 2, E211.
- Khan, O., J. H. Badhiwala, G. Grasso, M. G. Fehlings. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. – World Neurosurg, Vol. 140, 2020, pp. 512-518.
- Zhang, M., L. Zhu, S. Y. Lin, K. Herr, C. L. Chi, I. Demir, K. Dunn Lopez, N. C. Chi. Using Artificial Intelligence to Improve Pain Assessment and Pain Management: A Scoping Review. – Journal of the American Medical Informatics Association, Vol. 30, 2023, No 3, pp. 570-587.
- Rodriguez, P., G. Cucurull, J. Gonzàlez, J. M. Gonfaus, K. Nasrollahi, T. B. Moeslund, F. X. Roca. Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification. – IEEE Transactions on Cybernetics, Vol. 52, 2017, No 5, pp. 3314-3324.
- Cohen, S. P., L. Vase, W. M. Hooten. Chronic Pain: An Update on Burden, Best Practices, and New Advances. – The Lancet, Vol. 397, 2021, No 10289, pp. 2082-2097.
- Li, H., J. T. Moon, V. Shankar, J. Newsome, J. Gichoya, Z. Bercu. Health Inequities, Bias, and Artificial Intelligence. – Techniques in Vascular and Interventional Radiology, Vol. 27, 2024, No 3, 100990.
- Patel, P. M., M. Green, J. Tram, E. Wang, M. Z. Murphy, A. Abd-Elsayed, K. Chakravarthy. Beyond the Pain Management Clinic: The Role of AI-Integrated Remote Patient Monitoring in Chronic Disease Management – A Narrative Review. – Journal of Pain Research, 31 December 2024, pp. 4223-4237.
- El-Tallawy, S. N., J. V. Pergolizzi, I. Vasiliu-Feltes, R. S. Ahmed, J. K. LeQuang, T. Alzahrani, G. Varrassi, F. I. Awaleh, A. T. Alsubaie, M. S. Nagiub. Innovative Applications of Telemedicine and Other Digital Health Solutions in Pain Management: A Literature Review. – Pain and Therapy, Vol. 13, 2024, No 4, pp. 791-812.
- Thiam, P., H. Hihn, D. A. Braun, H. A. Kestler, F. Schwenker. Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective. – Frontiers in Physiology, Vol. 12, 2021, 720464.
- Phan, K. N., N. K. Iyortsuun, S. Pant, H. J. Yang, S. H. Kim. Pain Recognition with Physiological Signals Using Multi-Level Context Information. – IEEE Access, Vol. 11, 2023, pp. 20114-20127.
- Kumar, V. M. Discriminating the Infant Cry Sounds due to Pain vs Discomfort towards Assisted Clinical Diagnosis. – In: Proc. of 7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT’16), 2016.
- Bellmann, P., P. Thiam, H. A. Kestler, F. Schwenker. Machine Learning-Based Pain Intensity Estimation: Where Pattern Recognition Meets Chaos Theory – An Example Based on the Biovid Heat Pain Database. – IEEE Access, Vol. 10, 2022, pp. 102770-102777.
- Borna, S., C. R. Haider, K. C. Maita. A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence. – Bioengineering, Vol. 10, 2023, No 4, 500.
- Velez, J. C., L. E. Friedman, C. Barbosa, J. Castillo, D. L. Juvinao-Quintero, M. A. Williams, B. Gelaye. Evaluating the Performance of the Pain Interference Index and the Short Form McGill Pain Questionnaire among Chilean Injured Working Adults. – Plos one, Vol. 17, 2022, No 5, e0268672.
- El-Tallawy, S. N., R. S. Ahmed, M. S. Nagiub. Pain Management in the Most Vulnerable Intellectual Disability: A Review. – Pain and Therapy, Vol. 12, 2023, No 4, pp. 939-961.
- Ji, C., T. B. Mudiyanselage, Y. Gao, Y. Pan. A Review of Infant Cry Analysis and Classification. – EURASIP Journal on Audio, Speech, and Music Processing, Vol. 2021, 2021, No 1.
- Aqajari, S. A., R. Cao, E. Kasaeyan Naeini, M. D. Calderon, K. Zheng, N. Dutt, P. Liljeberg, S. Salanterä, A. M. Nelson, A. M. Rahmani. Pain Assessment Tool with Electrodermal Activity for Postoperative Patients: Method Validation Study. – JMIR mHealth and uHealth, Vol. 9, 2021, No 5, e25258.
- Segato, A., A. Marzullo, F. Calimeri, E. de Momi. Artificial Intelligence for Brain Diseases: A Systematic Review. – APL Bioengineering, Vol. 4, 2020, No 4.
- Thiam, P., H. Hihn, D. A. Braun, H. A. Kestler, F. Schwenker. Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective. – Frontiers in Physiology, Vol. 12, 2021, 720464.
- Yu, S., W. Wu. Multimodal Non-Invasive Non-Pharmacological Therapies for Chronic Pain: Mechanisms and Progress. – BMC Medicine, Vol. 21, 2023, No 1, 372.
- Baron, R., A. Binder, G. Wasner. Neuropathic Pain: Diagnosis, Pathophysiological Mechanisms, and Treatment. – The Lancet Neurology, Vol. 9, 2010, No 8, pp. 807-819.
- Salama, V., B. Godinich, Y. Geng, L. Humbert-Vidan, L. Maule, K. A. Wahid, M. A. Naser, R. He, A. S. Mohamed, C. D. Fuller, A. C. Moreno. Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review. – Journal of Pain and Symptom Management, 3 August 2024.
- Zamzmi, G., R. Kasturi, D. Goldgof, R. Zhi, T. Ashmeade, Y. Sun. A Review of Automated Pain Assessment in Infants: Features, Classification Tasks, and Databases. – IEEE Reviews in Biomedical Engineering, Vol. 11, 2017, pp. 77-96.
- Lammers, C. R., A. J. Schwinghammer, B. Hall, R. S. Kriss, D. A. Aizenberg, J. L. Funamura, C. W. Senders, V. Nittur, R. L. Applegate. Comparison of Oral Loading Dose to Intravenous Acetaminophen in Children for Analgesia after Tonsillectomy and Adenoidectomy: A Randomized Clinical Trial. – Anesthesia & Analgesia, Vol. 133, 2021, No 6, pp. 1568-1576.
- El-Tallawy, S. N., R. Nalamasu, G. I. Salem, J. A. LeQuang, J. V. Pergolizzi, P. J. Christo. Management of Musculoskeletal Pain: An Update with Emphasis on Chronic Musculoskeletal Pain. – Pain and Therapy, Vol. 10, 2021, pp. 181-209.