Post-Doctoral Fellowship on Automatic speech disorders detection during awake brain surgery
18 months possibly renewable at LaTIM (UMR INSERM 1101)
LaTIM UMR 1101 (Laboratory of Medical Information Processing)
INSERM (National Institute of Health and Medical Research)
LabEx CAMI (Computer Assisted Medical Interventions)
LTSI (Laboratory of Signal and Image Processing)
Post-Doctoral Fellowship
Low-grade gliomas (LGG, WHO grade II) are infiltrative brain tumors that generally occur in young patients (average age 38 years). Due to their slow growth and the plasticity of the brain, most patients have a normal or almost normal clinical examination for several years. Surgery is considered the best treatment for LGG due to its impact on the time to anaplastic transformation and improved survival rate. When resecting LGGs located in eloquent areas, intraoperative mapping of electrical stimulation under awake conditions has been shown to be essential for localizing the patient's neurological functions, as it allows the surgeon to maximize the extent of resection while reducing the risk of permanent deficits [1, 2]. The localization of these functions is based on neuropsychological tests that are carried out just after cortical and subcortical stimulations during surgery. The awake patient is thus invited to perform diRerent exercises depending on the areas stimulated. Electrical stimulation causes a reversible lesion in the targeted area and allows medical staR to analyze the patient's response by identifying possible neuropsychological disorders linked to these areas. Therefore, if a deficit occurs in the patient’s response, the stimulated area is labeled as eloquent and is preserved. Since 2015, every Awake Craniotomies (AC) performed at the Tokyo Women’s Medical University Hospital are recorded using the Intraoperative Examination Monitor for Awake Surgery (IEMAS) [3] which allows the acquisition of the patient’s face and voice, the task currently administered and the video of the patient’s brain, indicating when and where the surgeon is performing a stimulation. Since May 2023, every AC from the University Hospital of Brest are also recorded using the software presented in [4] to initiate the constitution of a French dedicated awake brain surgery database including vital signs, stimulation parameters and the patient’s speech during exercises [4]. With the objective of optimizing tumor resection, our goal is to develop algorithms and digital solutions to further help the medical team by better detecting and identifying patient's intraoperative deficits following stimulation, starting with language for AC.
Numerous works on the detection of speech disorders have been published in the literature. They mainly concern stuttering [5], dysarthria [6] and Parkinson's disease [7]. Except for the work of Nishimura et al [8] carried out by the “Center for Advanced Biomedical Sciences” (TWIns), a joint research facility between Tokyo Women's Medical University and Waseda University, no research has been focused on speech disorders caused by intraoperative stimulation. In addition, all this work was carried out using English-language databases only. Based on the joint experiences between the LaTIM, LTSI and the Twins teams, our objective will be to analyze the correlations between speech alteration and the exact location of the stimulation. For that, the goal of this post-doc position will be to:
This post-doc position will be mainly hosted in the LaTIM (Brest, France). Born from the complementarity between health and data science, the LaTIM laboratory develops multidisciplinary research driven by members from IMT Atlantique, CHRU Brest, University of Western Brittany and Inserm. But several mobilities will also be done either to the MediCIS team (https://medicis.univ-rennes1.fr/) of the LTSI lab (Rennes, France) or/and to the Twins (https://www.twmu.ac.jp/ABMES/FATS/) joint research facility (Tokyo, Japan).
PhD in language processing, AI, applied mathematics.
Excellent programming, especially in python and C++.
Good English skills.
High motivation for publications and for international mobilities.
CV with list of publications, cover letter and two letters of recommendation, must be sent to Guillaume Dardenne, LaTIM (guillaume.dardenne@inserm.fr)
The position is available as soon as possible for 18 months, contract possibly renewable.
The salary will depend on the candidate’s experience.
[1] Du'au, H. (2012). The challenge to remove di'use low-grade gliomas while preserving brain functions. Acta neurochirurgica, 154, 569-574.
[2] So'ietti, R., Baumert, B. G., Bello, L., Von Deimling, A., Du'au, H., Frénay, M., ... & Wick, W. (2010). Guidelines on management of low-grade gliomas: report of an EFNS–EANO* Task Force. European journal of neurology, 17(9), 1124-1133.
[3] Yoshimitsu, K., Suzuki, T., Muragaki, Y., Chernov, M., & Iseki, H. (2010, August). Development of modified intraoperative examination monitor for awake surgery (IEMAS) system for awake craniotomy during brain tumor resection. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (pp. 6050-6053). IEEE.
[4] Maoudj, I., Garraud, C., Panheleux, C., Saliou, V., Seizeur, R., & Dardenne, G. (2023, July). A modular system for the synchronized multimodal data acquisition during Awake Surgery: towards the emergence of a dedicated clinical database. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1-4). IEEE.
[5] Sheikh, S. A., Sahidullah, M., Hirsch, F., & Ouni, S. (2022). Machine learning for stuttering identification: Review, challenges and future directions. Neurocomputing.
[6] Sekhar, S. M., Kashyap, G., Bhansali, A., & Singh, K. (2022). Dysarthric-speech detection using transfer learning with convolutional neural networks. ICT Express, 8(1), 61-64.
[7] Laila, R., Salwa, L., & Mohammed, R. (2021, April). Detection of voice impairment for parkinson's disease using machine learning tools. In 2020 10th International Symposium on Signal, Image, Video and Communications (ISIVC) (pp. 1-6). IEEE.
[8] Nishimura, T., Nagao, T., Iseki, H., Muragaki, Y., Tamura, M., & Minami, S. (2014, November). Classification of patient's reaction in language assessment during awake craniotomy. In 2014 IEEE 7th International Workshop on Computational Intelligence and Applications (IWCIA) (pp.207-212). IEEE.
[9] Tokuda, J., Fischer, G. S., Papademetris, X., Yaniv, Z., Ibanez, L., Cheng, P., ... & Hata, N. (2009). OpenIGTLink: an open network protocol for image-guided therapy environment. The International Journal of Medical Robotics and Computer Assisted Surgery, 5(4), 423-434.
[10] Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., ... & Bakas, S. (2021). The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314.
[11] Maoudj, I., Kuwano, A., Panheleux, C., Kubota, Y., Kawamata, T., Muragaki, Y., ... Dardenne, G., Tamura, M. (2024). Classification of Speech Arrests and Speech Impairments during Awake Craniotomy: a multi-databases analysis.
[12] Kourkounakis, T., Hajavi, A., & Etemad, A. (2020). FluentNet: end-to-end detection of speech disfluency with deep learning. arXiv preprint arXiv:2009.11394.
[13] Sheikh, S. A., Sahidullah, M., Hirsch, F., & Ouni, S. (2022). Machine learning for stuttering identification: Review, challenges and future directions. Neurocomputing, 514, 385-402.
(c) GdR IASIS - CNRS - 2024.