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1. Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients

   https://doi.org/10.1109/ICHI57859.2023.00047  

   Wang, Yaohua; Dijk, Lisanne Van; Mohamed, Abdallah_S R; Naser, Mohamed; Fuller, Clifton David; Zhang, Xinhua; Marai, G Elisabeta; Canahuate, Guadalupe (June 2023, IEEE Xplore)

   Patient-Reported Outcomes (PRO) are collected directly from the patients using symptom questionnaires. In the case of head and neck cancer patients, PRO surveys are recorded every week during treatment with each patient’s visit to the clinic and at different follow-up times after the treatment has concluded. PRO surveys can be very informative regarding the patient’s status and the effect of treatment on the patient’s quality of life (QoL). Processing PRO data is challenging for several reasons. First, missing data is frequent as patients might skip a question or a questionnaire altogether. Second, PROs are patient-dependent, a rating of 5 for one patient might be a rating of 10 for another patient. Finally, most patients experience severe symptoms during treatment which usually subside over time. However, for some patients, late toxicities persist negatively affecting the patient’s QoL. These long-term severe symptoms are hard to predict and are the focus of this study. In this work, we model PRO data collected from head and neck cancer patients treated at the MD Anderson Cancer Center using the MD Anderson Symptom Inventory (MDASI) questionnaire as time series. We impute missing values with a combination of K nearest neighbor (KNN) and Long Short-Term Memory (LSTM) neural networks, and finally, apply LSTM to predict late symptom severity 12 months after treatment. We compare performance against clinical and ARIMA models. We show that the LSTM model combined with KNN imputation is effective in predicting late-stage symptom ratings for occurrence and severity under the AUC and F1 score metrics. 

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2. DASS Good: Explainable Data Mining of Spatial Cohort Data

   https://doi.org/10.1111/cgf.14830  

   Wentzel, A.; Floricel, C.; Canahuate, G.; Naser, M. A.; Mohamed, A. S.; Fuller, CD.; van Dijk, L.; Marai, G. E. (June 2023, Computer Graphics Forum)

   Abstract Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co‐design of a modeling system, DASS, to support the hybrid human‐machine development and validation of predictive models for estimating long‐term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human‐in‐the‐loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience. 

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3. A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery

   https://doi.org/10.1002/hed.27674  

   Jiam, Max L; Xin, Kevin Z; Ha, Patrick K; Jiam, Nicole T (May 2024, Head & Neck)

   Abstract BackgroundNew patient referrals are often processed by practice coordinators with little‐to‐no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection. MethodsA retrospective cohort study was conducted on 64 222 patient datapoints from the SEER database. ResultsThe random forest ML model correctly classified patients who were offered head and neck surgery with an 81% accuracy rate. The sensitivity and specificity rates were 86% and 71%. The positive and negative predictive values were 85% and 73%. ConclusionsML modeling accurately predicts head and neck cancer surgery recommendations based on patient and cancer information from a large population‐based dataset. ML adjuncts for referral processing may decrease the time to treatment for patients with cancer. 

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4. Airway obstruction secondary to vocal cord polyp

   https://doi.org/10.1097/01.JAA.0000000000000124  

   Filarski, Carolyn F; Donald, Carrlene B; Mendez, Anthony P; Lott, David G (January 2024, JAAPA)

   ABSTRACT Patients with vocal cord polyps commonly present with symptoms of hoarseness. Although rare, large polyps can cause shortness of breath and stridor and should be included in the differential for patients with airway obstruction. Dysphonia or hoarseness can be a symptom of underlying disease, such as head and neck cancer. This case illustrates the importance of prompt and accurate diagnosis in a patient with persistent symptoms and a history of smoking. Obtaining a laryngoscopy is crucial to appropriately evaluate the larynx. Proper visualization of the laryngeal structures will help direct patient care toward further diagnostic imaging and medical or surgical intervention if indicated. 

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5. A six degrees-of-freedom cable-driven robotic platform for head–neck movement

   https://doi.org/10.1038/s41598-024-59349-0  

   Bales, Ian; Zhang, Haohan (April 2024, Scientific Reports)

   Abstract This paper introduces a novel cable-driven robotic platform that enables six degrees-of-freedom (DoF) natural head–neck movements. Poor postural control of the head–neck can be a debilitating symptom of neurological disorders such as amyotrophic lateral sclerosis and cerebral palsy. Current treatments using static neck collars are inadequate, and there is a need to develop new devices to empower movements and facilitate physical rehabilitation of the head–neck. State-of-the-art neck exoskeletons using lower DoF mechanisms with rigid linkages are limited by their hard motion constraints imposed on head–neck movements. By contrast, the cable-driven robot presented in this paper does not constrain motion and enables wide-range, 6-DoF control of the head–neck. We present the mechatronic design, validation, and control implementations of this robot, as well as a human experiment to demonstrate a potential use case of this versatile robot for rehabilitation. Participants were engaged in a target reaching task while the robot applied both assistive and resistive moments on the head during the task. Our results show that neck muscle activation increased by 19% when moving the head against resistance and decreased by 28–43% when assisted by the robot. Overall, these results provide a scientific justification for further research in enabling movement and identifying personalized rehabilitation for motor training. Beyond rehabilitation, other applications such as applying force perturbations on the head to study sensory integration and applying traction to achieve pain relief may benefit from the innovation of this robotic platform which is capable of applying controlled 6-DoF forces/moments on the head. 

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