{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T14:18:53Z","timestamp":1771337933229,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002753","name":"Universidad Nacional de Colombia","doi-asserted-by":"publisher","award":["57661"],"award-info":[{"award-number":["57661"]}],"id":[{"id":"10.13039\/501100002753","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Maternal health care during labor requires the continuous and reliable monitoring of analgesic procedures, yet conventional systems are often subjective, indirect, and operator-dependent. Infrared thermography (IRT) offers a promising non-invasive approach for labor epidural analgesia (LEA) monitoring, but its practical implementation is hindered by clinical and hardware limitations. This work presents a novel artificial intelligence-driven mobile platform to overcome these hurdles. The proposed solution integrates a lightweight deep learning model for semantic segmentation, a B-spline-based free-form deformation (FFD) approach for non-rigid dermatome registration, and efficient on-device inference. Our analysis identified a U-Net with a MobileNetV3 backbone as the optimal architecture, achieving a high Dice score of 0.97 and a 4.5% intersection over union (IoU) gain over heavier backbones while being 73% more parameter-efficient. The entire AI pipeline is deployed on a commercial smartphone via TensorFlow Lite, achieving an on-device inference time of approximately two seconds per image. Deployed within a user-friendly interface, our approach provides straightforward feedback to support decision making in labor management. By integrating thermal imaging with deep learning and mobile deployment, the proposed system provides a practical solution to enhance maternal care. By offering a quantitative, automated tool, this work demonstrates a viable pathway to augment or replace subjective clinical assessments with objective, data-driven monitoring, bridging the gap between advanced AI research and point-of-care practice in obstetric anesthesia.<\/jats:p>","DOI":"10.3390\/computers14110466","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:32:01Z","timestamp":1762191121000},"page":"466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence-Driven Mobile Platform for Thermographic Imaging to Support Maternal Health Care"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1057-9095","authenticated-orcid":false,"given":"Lucas Miguel","family":"Iturriago-Salas","sequence":"first","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1716-5015","authenticated-orcid":false,"given":"Jeison Andres","family":"Mesa-Sarmiento","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4442-0715","authenticated-orcid":false,"given":"Paola Alexandra","family":"Castro-Cabrera","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0308-9576","authenticated-orcid":false,"given":"Andr\u00e9s Marino","family":"\u00c1lvarez-Meza","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0138-5489","authenticated-orcid":false,"given":"German","family":"Castellanos-Dominguez","sequence":"additional","affiliation":[{"name":"Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S1260","DOI":"10.1016\/j.ajog.2022.06.017","article-title":"Modern labor epidural analgesia: Implications for labor outcomes and maternal-fetal health","volume":"228","author":"Callahan","year":"2023","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sia, A., Sng, B.L., Ramage, S., Armstrong, S., and Sultan, P. (2022). Failed epidural analgesia during labour. Quick Hits in Obstetric Anesthesia, Springer.","DOI":"10.1007\/978-3-030-72487-0_54"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1002\/ijgo.14175","article-title":"Epidural analgesia in labor: A narrative review","volume":"159","author":"Halliday","year":"2022","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1080\/08998280.2024.2403937","article-title":"A standardized algorithm for assessing labor epidural analgesia","volume":"37","author":"Anyaehie","year":"2024","journal-title":"Bayl. Univ. Med. Cent. Proc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111079","DOI":"10.1016\/j.buildenv.2023.111079","article-title":"Non-invasive infrared thermography technology for thermal comfort: A review","volume":"248","author":"Zheng","year":"2024","journal-title":"Build. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102981","DOI":"10.1016\/j.ijoa.2021.102981","article-title":"Use of high-resolution thermography as a validation measure to confirm epidural anesthesia in mice: A cross-over study","volume":"46","author":"Xu","year":"2021","journal-title":"Int. J. Obstet. Anesth."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Topalidou, A., Markarian, G., and Downe, S. (2020). Thermal imaging of the fetus: An empirical feasibility study. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0226755"},{"key":"ref_8","unstructured":"Whitman, P.A., Launico, M.V., and Adigun, O.O. (2024). Anatomy, Skin, Dermatomes, StatPearls Publishing."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"K\u00fct\u00fck, Z., and Algan, G. (2022, January 18\u201324). Semantic segmentation for thermal images: A comparative survey. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00043"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Soliz, P., Duran-Valdez, E., Saint-Lot, S., Kurup, A., Bancroft, A., and Schade, D.S. (November, January 31). Functional Thermal Video Imaging of the Plantar Foot for Identifying Biomarkers of Diabetic Peripheral Neuropathy. Proceedings of the 2022 56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/IEEECONF56349.2022.10051956"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pakarinen, T., Oksala, N., and Vehkaoja, A. (2024). Confounding factors in peripheral thermal recovery time after active cooling. J. Therm. Biol., 121.","DOI":"10.1016\/j.jtherbio.2024.103826"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bougrine, A., Harba, R., Canals, R., Ledee, R., and Jabloun, M. (2019, January 2\u20136). On the segmentation of plantar foot thermal images with Deep Learning. Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), A Coru\u00f1a, Spain.","DOI":"10.23919\/EUSIPCO.2019.8902691"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101475","DOI":"10.1016\/j.inat.2021.101475","article-title":"Use of smartphone-integrated infrared thermography to monitor sympathetic dysfunction as a surgical complication","volume":"28","author":"Germi","year":"2022","journal-title":"Interdiscip. Neurosurg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/03091902.2022.2077997","article-title":"Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet)","volume":"46","author":"Bouallal","year":"2022","journal-title":"J. Med. Eng. Technol."},{"key":"ref_15","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20230146","DOI":"10.1002\/EXP.20230146","article-title":"Neural interfaces: Bridging the brain to the world beyond healthcare","volume":"4","author":"Xu","year":"2024","journal-title":"Exploration"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zang\u00e3o, M.O.B., Poeira, A.F., Branco, M., and Santos-Rocha, R. (2024). Changes in foot biomechanics during pregnancy and postpartum: Scoping review. Int. J. Environ. Res. Public Health, 21.","DOI":"10.3390\/ijerph21050638"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103385","DOI":"10.1016\/j.media.2024.103385","article-title":"A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond","volume":"100","author":"Chen","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_19","first-page":"1","article-title":"The Infrared Thermography Toolbox: An Open-access Semi-automated Segmentation Tool for Extracting Skin Temperatures in the Thoracic Region including Supraclavicular Brown Adipose Tissue","volume":"46","author":"Straat","year":"2022","journal-title":"J. Med. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, H.J., Lee, C.Y., Lai, J.H., Chang, Y.C., and Chen, C.M. (2022). Image registration method using representative feature detection and iterative coherent spatial mapping for infrared medical images with flat regions. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-11379-2"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"668","DOI":"10.9745\/GHSP-D-20-00685","article-title":"Using mHealth to improve timeliness and quality of maternal and newborn health in the primary health care system in Ethiopia","volume":"9","author":"Nigussie","year":"2021","journal-title":"Glob. Health Sci. Pract."},{"key":"ref_22","first-page":"1","article-title":"Empowering edge intelligence: A comprehensive survey on on-device ai models","volume":"57","author":"Wang","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_23","first-page":"e86892","article-title":"The Novel Introduction of a Thermal Camera in the Retrieval of a Retained Rectus Sheath Catheter: A Case Study","volume":"17","author":"Awaly","year":"2025","journal-title":"Cureus"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101504","DOI":"10.1016\/j.imu.2024.101504","article-title":"Deep learning for medical image segmentation: State-of-the-art advancements and challenges","volume":"47","author":"Rayed","year":"2024","journal-title":"Inform. Med. Unlocked"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alshayeji, M.H., Sindhu, S.C., and Abed, S. (2023). Early detection of diabetic foot ulcers from thermal images using the bag of features technique. Biomed. Signal Process. Control, 79.","DOI":"10.1016\/j.bspc.2022.104143"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103245","DOI":"10.1016\/j.ijoa.2021.103245","article-title":"Infrared thermographic assessment of spinal anaesthesia-related cutaneous temperature changes during caesarean section","volume":"49","author":"Murphy","year":"2022","journal-title":"Int. J. Obstet. Anesth."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nowakowski, A.Z., and Kaczmarek, M. (2025). Artificial intelligence in IR thermal imaging and sensing for medical applications. Sensors, 25.","DOI":"10.3390\/s25030891"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, S., Liu, Y., Liu, X., Liu, T., Li, P., and Mei, W. (2021). Infrared thermography for assessment of thoracic paravertebral block: A prospective observational study. BMC Anesthesiol., 21.","DOI":"10.1186\/s12871-021-01389-4"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ijoa.2019.08.006","article-title":"Infrared thermography to assess dermatomal levels of labor epidural analgesia with 1 mg\/mL ropivacaine plus 0.5 \u03bcg\/mL sufentanil: A prospective cohort study","volume":"41","author":"Bouvet","year":"2020","journal-title":"Int. J. Obstet. Anesth."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104304","DOI":"10.1016\/j.ijoa.2024.104304","article-title":"Infrared thermographic assessment of cutaneous temperature changes during labour epidural analgesia initiation: An observational pilot study","volume":"62","author":"Miglani","year":"2025","journal-title":"Int. J. Obstet. Anesth."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"200","DOI":"10.25237\/revchilanestv54n2-14","article-title":"Precisi\u00f3n diagn\u00f3stica de la termograf\u00eda para evaluar analgesia epidural en gestantes","volume":"54","author":"Daza","year":"2025","journal-title":"Rev. Chil. Anestesiol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xue, H., Shen, M., Sun, Y., Tian, H., Liu, Z., Chen, J., and Xu, P. (2023). Instance segmentation and ensemble learning for automatic temperature detection in multiparous sows. Sensors, 23.","DOI":"10.3390\/s23229128"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Villa, E., Arteaga-Marrero, N., and Ruiz-Alzola, J. (2020). Performance assessment of low-cost thermal cameras for medical applications. Sensors, 20.","DOI":"10.3390\/s20051321"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Putrino, A., Cassetta, M., Raso, M., Altieri, F., Brilli, D., Mezio, M., Circosta, F., Zaami, S., and Marinelli, E. (2024). Clinical Applications, Legal Considerations and Implementation Challenges of Smartphone-Based Thermography: A Scoping Review. J. Clin. Med., 13.","DOI":"10.3390\/jcm13237117"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.procs.2020.05.045","article-title":"Development of low-cost thermal imaging system as a preliminary screening instrument","volume":"172","author":"Haripriya","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10877-017-0026-y","article-title":"Thermographic skin temperature measurement compared with cold sensation in predicting the efficacy and distribution of epidural anesthesia","volume":"32","author":"Bruins","year":"2018","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e311","DOI":"10.1093\/rheumatology\/kead210","article-title":"Mobile phone thermal imaging as an ambulatory assessment tool in Raynaud\u2019s phenomenon","volume":"62","author":"Dinsdale","year":"2023","journal-title":"Rheumatology"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ryka\u0142a, K., Szurko, A., Wzi\u0105tek-Kuczmik, D., Kie\u0142bo\u0144, A., Sillero-Quintana, M., Cholewka, A., and Kasprzyk-Kucewicz, T. (2025). Thermal Imaging as a New Perspective in the Study of Physiological Changes in Pregnant Women\u2014A Preliminary Study. J. Clin. Med., 14.","DOI":"10.3390\/jcm14175998"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"15273","DOI":"10.1007\/s11042-018-7113-z","article-title":"Automated approaches for ROIs extraction in medical thermography: A review and future directions","volume":"79","author":"Singh","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"161296","DOI":"10.1109\/ACCESS.2019.2951356","article-title":"Plantar Thermogram Database for the Study of Diabetic Foot Complications","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, Z., Zeng, Z., Xie, J., Zhai, H., Yin, Y., Ma, Y., and Tian, Y. (2023). Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network. Sensors, 23.","DOI":"10.3390\/s23208511"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bougrine, A., Harba, R., Canals, R., Ledee, R., Jabloun, M., and Villeneuve, A. (2022). Segmentation of Plantar Foot Thermal Images Using Prior Information. Sensors, 22.","DOI":"10.3390\/s22103835"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Khandakar, A., Chowdhury, M.E., Reaz, M.I., Ali, S.M., Hasan, M.K., Kiranyaz, S., Rahman, T., Alfkey, R., Bakar, A.A.A., and Malik, R.A. (2021). A machine learning model for early detection of diabetic foot using thermogram images. Comput. Biol. Med., 137.","DOI":"10.1016\/j.compbiomed.2021.104838"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Panamonta, V., Jerawatana, R., Ariyaprayoon, P., Looareesuwan, P., Ongphiphadhanakul, B., Sriphrapradang, C., Chailurkit, L., and Ongphiphadhanakul, B. (2025). Plantar thermogram analysis using deep learning for diabetic foot risk classification. J. Diabetes Sci. Technol.","DOI":"10.1177\/19322968251316563"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e65209","DOI":"10.2196\/65209","article-title":"The Use of AI-Powered Thermography to Detect Early Plantar Thermal Abnormalities in Patients with Diabetes: Cross-Sectional Observational Study","volume":"10","author":"Alwashmi","year":"2025","journal-title":"JMIR Diabetes"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, L. (2022). Non-rigid multi-modal medical image registration based on improved maximum mutual information PV image interpolation method. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.863307"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1504\/IJBET.2022.124662","article-title":"Non-rigid registration (computed tomography-ultrasound) of liver using B-splines and free form deformation","volume":"39","author":"Bhattacharjee","year":"2022","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1007\/s10278-022-00763-z","article-title":"Cascading affine and B-spline registration method for large deformation registration of lung X-rays","volume":"36","author":"Chang","year":"2023","journal-title":"J. Digit. Imaging"},{"key":"ref_52","unstructured":"Boussot, V., H\u00e9mon, C., Nunes, J.C., Dowling, J., Rouz\u00e9, S., Lafond, C., Barateau, A., and Dillenseger, J.L. (2025). IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zou, J., Gao, B., Song, Y., and Qin, J. (2022). A review of deep learning-based deformable medical image registration. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1047215"},{"key":"ref_54","unstructured":"Zhou, C., Li, X., Loy, C.C., and Dai, B. (2023). Edgesam: Prompt-in-the-loop distillation for on-device deployment of sam. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100502","DOI":"10.1016\/j.smhl.2024.100502","article-title":"Towards accurate Diabetic Foot Ulcer image classification: Leveraging CNN pre-trained features and extreme learning machine","volume":"33","author":"Arnia","year":"2024","journal-title":"Smart Health"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s10586-024-04686-y","article-title":"Edge AI: A taxonomy, systematic review and future directions","volume":"28","author":"Gill","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Han, T., Li, D., Liu, J., Tian, L., and Shan, Y. (2021, January 11\u201317). Improving low-precision network quantization via bin regularization. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00521"},{"key":"ref_58","first-page":"1","article-title":"Quantization of deep neural networks for accurate edge computing","volume":"17","author":"Chen","year":"2021","journal-title":"ACM J. Emerg. Technol. Comput. Syst. (JETC)"},{"key":"ref_59","first-page":"95","article-title":"Development and validation of android based mobile app for diabetic foot early self-assessment","volume":"22","author":"Agustini","year":"2022","journal-title":"Malays. J. Public Health Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"124373","DOI":"10.1109\/ACCESS.2022.3225107","article-title":"Performance evaluation of deep learning models for image classification over small datasets: Diabetic foot case study","volume":"10","author":"Fabelo","year":"2022","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"77","DOI":"10.12688\/openreseurope.14706.1","article-title":"STANDUP database of plantar foot thermal and RGB images for early ulcer detection","volume":"2","author":"Bouallal","year":"2022","journal-title":"Open Res. Eur."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Aguirre-Arango, J.C., \u00c1lvarez-Meza, A.M., and Castellanos-Dominguez, G. (2023). Feet segmentation for regional analgesia monitoring using convolutional RFF and layer-wise weighted CAM interpretability. Computation, 11.","DOI":"10.20944\/preprints202305.0670.v1"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Mejia-Zuluaga, R., Aguirre-Arango, J.C., Collazos-Huertas, D., Daza-Castillo, J., Valencia-Marulanda, N., Calder\u00f3n-Marulanda, M., Aguirre-Ospina, \u00d3., Alvarez-Meza, A., and Castellanos-Dominguez, G. (2022, January 23\u201325). Deep Learning Semantic Segmentation of Feet Using Infrared Thermal Images. Proceedings of the Ibero-American Conference on Artificial Intelligence, Cartagena de Indias, Colombia.","DOI":"10.1007\/978-3-031-22419-5_29"},{"key":"ref_64","unstructured":"Murphy, K.P. (2022). Probabilistic Machine Learning: An Introduction, MIT Press."},{"key":"ref_65","unstructured":"Mao, A., Mohri, M., and Zhong, Y. (2023, January 23\u201329). Cross-entropy loss functions: Theoretical analysis and applications. Proceedings of the International Conference on Machine Learning, Honolulu, HI, USA."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, R., Qian, B., Zhang, X., Li, Y., Wei, R., Liu, Y., and Pan, Y. (2020, January 17\u201320). Rethinking dice loss for medical image segmentation. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.","DOI":"10.1109\/ICDM50108.2020.00094"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., and Gholipour, A. (2017, January 10). Tversky loss function for image segmentation using 3D fully convolutional deep networks. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Shafiq, M., and Gu, Z. (2022). Deep residual learning for image recognition: A survey. Appl. Sci., 12.","DOI":"10.3390\/app12188972"},{"key":"ref_71","unstructured":"Bharati, S., Mondal, M., Podder, P., and Prasath, V. (2022). Deep learning for medical image registration: A comprehensive review. arXiv."},{"key":"ref_72","unstructured":"Rep\u00fablica de Colombia (2025, July 01). Ley Estatutaria 1581 de 2012: Por la cual se dictan disposiciones generales para la protecci\u00f3n de datos personales. Diario Oficial No. 48.178, Available online: https:\/\/www.funcionpublica.gov.co\/eva\/gestornormativo\/norma.php?i=49981."},{"key":"ref_73","unstructured":"(2019). Systems and Software Engineering: Systems and Software Quality Requirements and Evaluation (SQuaRE): Quality Requirements Framework (Standard No. ISO\/IEC 25030:2019)."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e49510","DOI":"10.2196\/49510","article-title":"Effectiveness of mHealth apps for maternal health care delivery: Systematic review of systematic reviews","volume":"26","author":"Ameyaw","year":"2024","journal-title":"J. Med. Internet Res."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/11\/466\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:46:17Z","timestamp":1762191977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/11\/466"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":74,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["computers14110466"],"URL":"https:\/\/doi.org\/10.3390\/computers14110466","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]}}}