EverythingALS is a patient-focused non-profit, part of Peter Cohen Foundation (PCF) a 501(3)c organization. Our mission is to support efforts to care for ALS patients and work to find a cure by creating a platform for direct engagement with patients, caregivers, advocates, and researchers. LOGIN > Click here to Register or Login if you are in the study You can help advance ALS Research Join one, or more, of our current studies ALS Path to Trials Multi-modal study to match you to trials A new IRB approved study that meets you where you are. Whether you can track motor function, speech, or passive data, you'll build a personalized digital baseline so that when the right trial opens, you're already ready to enroll. I’m Interested ALS Austen Study Advancing the Diagnosis and Prognosis of ALS from Speech Our IRB approved study is motivated by the need for early detection and improved prognostic accuracy of ALS using advanced computational technology and speech which includes both audio and video data. Click to Join ALS Gene Carrier Study Families Fight Together This study is focused on early detection of motor and speech changes using emerging digital technologies to support families affected by ALS and FTD. Click to Join Why Join the Study? Vivian Rojas If you are interested in the study, please contact us Your connection to ALS I am interested to participate in * Required The Speech Study to advance drug Trials The ALS Gene Carrier Study Path to Trials Are you enrolled in PREVENT ALS Study (DIALS Network at MGH/WashU or ALS Families Study at Columbia University)? Yes No Don't Know For more information click here . Do you use Assistive Devices ? For Walking For Breathing CPAP Other Cane Rollator Walker Wheelchair Other Where do you currently reside? Select your residence Submit

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About Us We believe the technology will be a key enabler for the innovation to end ALS, we are here to bridge the gap between patients, research and technology. Our diverse team is composed of patient advocates, students, nurses, physicians, entrepreneurs, artificial intelligence engineers, biologists, ALS patients, and spiritual guidance from one person who has reversed ALS. We are here to help provide information to all of those affected by ALS. EverythingALS is a patient-focused non-profit, part of Peter Cohen Foundation, a 501(3)c organization, bringing technological innovations and data science to support efforts -- from care to cure -- for people with ALS, by offering a open-data platform for direct engagement with patients, caregivers, researchers and drug companies. PC: Peter Cohen How YOU Can Get Involved Write to us, we would love to hear from you. How did you hear about us? How did you hear about us? How did you hear about us? Submit

Ask anything for People with ALS and Caregivers to help with ALS care to cure Future Events Expert Talks Series Fireside Chats Fireside Chat with the Pathfinders Wed, Sep 23 Virtual Event REGISTER Fireside Chat with the Pathfinders Wed, Oct 21 Virtual Event REGISTER Fireside Chat with the Pathfinders Wed, Nov 18 Virtual Event REGISTER Fireside Chat with the Pathfinders Wed, Dec 16 Virtual Event REGISTER

EverythingALS is a patient-focused non-profit, part of Peter Cohen Foundation (PCF) a 501(3)c organization. Our mission is to support efforts to care for ALS patients and work to find a cure by creating a platform for direct engagement with patients, caregivers, advocates, and researchers. LOGIN > Click here to Register or Login if you are in the study Advancing the Diagnosis and Prognosis of ALS from Speech WE ARE LOOKING FOR Individuals Diagnosed or Probable with Amyotrophic Lateral Sclerosis (ALS) and Healthy Participants to help give 5 speech r ecordings with total 75 minutes over 5 months Our IRB approved study is motivated by the need for early detection and improved prognostic accuracy of ALS using advanced computational technology and speech data (audio, video). By participating in this study, you will contribute to a growing large ALS dataset and further advance current knowledge relating to the decline in speech due to ALS while also improving the performance of this technology. The approach will be to perform analysis of online audio/video recordings: The study activity involves the use of Modality.ai Inc.’s web-based software that collects speech audio and video data and then uses AI and machine learning algorithms to analyze facial and speech metrics. Data collection can be conducted anywhere you feel comfortable (e.g., your home). One session per month over 5 months, and each session will last approximately fifteen minutes. Meet Amazing Vivian Rojas Diagnosed with ALS in 2018 If you are interested in the study, please contact us Your connection to ALS I am interested to participate in * Required The Speech Study to advance drug trials The Radcliff : Multi-disciplinary Study The ALS Gene Carrier Study Are you enrolled in PREVENT ALS Study (DIALS Network at MGH/WashU or ALS Families Study at Columbia University)? Yes No Don't Know For more information click here . What is your Shoe size? What is your T-shirt size? Do you use Assistive Devices ? For Walking For Breathing Women Men Breathing Device CPAP Other Cane Rollator Walker Wheelchair Other Country Submit Protocol Number: 2020-06-PI42 | Sponsor: Peter Cohen Foundation Contact Us Email: speech@everythingals.org

Careers Join us to help everyone impacted by ALS participate in Citizen-Driven Research and benefit from Open Innovation. EverythingALS offers satisfying careers for people ready to bring their talent and skills to the fight of finding a cure for ALS. We also offer critically important volunteer opportunities that empower all kinds of people to share their unique gifts to help drive our mission forward Boston Area, Remote Community Engagement Manager Reporting to the Executive Director, we are seeking a Community Engagement Manager with excellent project management and digital communications skills to join our dynamic team. This role is crucial in driving our discovery and engagement with our.. Read More

EverythingALS Publications Publications 2026 MDA Poster AI-Assisted ECAS Copilot to Support Test Administration and Improve Standardization in ALS Cognitive Screening Anusha Rao, Raquel Garcia, Silviya Bastola, Lauren Gray, Indu Navar, Sharon Abrahams, Ammar Al-Chalabi, Natalia Luchkina Abstract Cognitive and behavioral impairment affects up to 50% of people with amyotrophic lateral sclerosis (ALS). The Edinburgh Cognitive and Behavioural ALS Screen (ECAS) is an ALS-specific assessment that requires real-time transcription and structured scoring, introducing potential inter-rater variability. We developed a human-in-the-loop AI copilot to support ECAS administration with live transcription and guideline-based scoring suggestions while maintaining clinician oversight. In two retrospective datasets, high variability and error rates were observed in subtests requiring transcription and semantic judgment, In contrast, AI-assisted scoring produced fully consistent results for identical responses. Our ECAS copilot may improve scoring consistency, reduce administrator burden, and support remote assessment, which participants generally preferred and which substantially lowers environmental impact. A prospective validation study is currently ongoing. READ MORE 2025 Community Perspective Critical Bottlenecks in Rare Disease Research and Care: A Community Perspective Julie McMurry, Alison Sizer, Robert Allaway, Cornelius Boerkoel, AJ Chen, Jason Colquitt, James Cummings, Autri Dutta, Nasha Fitter, Robert Green, Adam Hansen, Eric Harker, Nomi Harris, Collin Hovinga, Ari Kahn, Hans Keil, Rodger Kessler, Lukas Lange, Arturo Loaiza-Bonilla, Natalia Luchkina, Eric Luellen, Arezoo Movaghar, Tomi Pastinen, Elizabeth Rountree, Vivek Rudrapatna, Adam Sand, Katharina Schmolly, Patrick Short, Marina Sirota, Geoffrey Siwo, Nicholas Tatonetti, Natan Vidra, Farid Vij, Samuel Volchenboum, Anita Walden, Ramona L. Walls, Peter Washington, Dylan Wenzlau, Margaret Wenzlau, Matthew Wheeler, Charlene Son Rigby, Melissa Haendel Abstract This document details an impromptu community gathering following the cancellation of an ARPA-H proposer's day for the Rare Disease AI/ML for Precision Integrated Diagnostics (RAPID) program . The discussion became a powerful example of how shared commitment to improving patient outcomes can transcend institutional boundaries and administrative hurdles. This white paper synthesizes perspectives from healthcare providers, academic researchers, industry experts, registry providers, and people with lived experience to identify critical bottlenecks that must be addressed to accelerate progress in the rare disease field. READ MORE 2025 NEALS Poster Evaluating the Precision of Quantitative Voice Characteristics as Endpoints in ALS Clinical Trials Macklin EA, Kim M, Sharma S, Taitz A, Peller J, Ostrow LW, Fraenkel E, Paganoni S, Cudkowicz ME, Shefner JM, Vogel AP, Navar Bingham I, Berry JD for the HEALEY ALS Platform Trial Study Group Abstract Progressive bulbar dysfunction, including dysarthria, is a common feature of ALS. Quantitative voice characteristics have been proposed as low-burden digital biomarkers of disease progression and potential pharmacodynamic biomarkers in ALS clinical trials. We used data from the first four regimens of the HEALEY ALS Platform Trial, where participants completed structured speech recordings in clinic and at home over 24 weeks. Multiple voice metrics were derived alongside independent assessments of listener effort, naturalness, and intelligibility. Listener effort, which measures communication through speech, showed high relative precision, whether rated by speech-language pathologists or estimated using a machine-learning model. Among low-level metrics, articulatory precision and pause rate had the highest relative precision. Restricting speech recordings to only in-clinic or only at-home settings, or reducing assessment frequency, increased variance, with an inflection when sampling less frequently than every 14 days. These findings highlight the importance of selecting sensitive metrics, maintaining frequent assessments, and optimizing cohort selection to advance speech-based endpoints in ALS trials. READ MORE 2025 ASHA Presentation Hidden in Plain Sight: Unlocking the Potential of SLP Expertise for Clinical Trial Advancement Designated as Centennial Session Centennial sessions are characterized by their ability to foresee and address future challenges, shape industry practices, and lead transformative initiatives. Katie Seaver, Lyle Ostrow, Donna Harris, Kathryn Wright, Jordan Green Abstract This session highlights the critical expertise of Speech-Language Pathologists (SLPs) in unlocking the potential of perceived listener effort (LE) as a clinically meaningful outcome measure for Amyotrophic Lateral Sclerosis (ALS).  We will explore the EverythingALS Speech Study, which leverages remote speech recordings and expert SLP ratings of LE to monitor dysarthria progression. Learn how LE provides a more sensitive and holistic assessment of communication impairment compared to traditional measures of intelligibility, severity, or the ALS Functional Rating Scale-R.  Discover the reliability of SLP scoring across different datasets and the potential of LE to serve as a valuable endpoint in ALS clinical trials, emphasizing the indispensable role of SLPs in advancing research and drug development in this field. READ MORE ENCALS LEPM Poster Machine Learning Model Predicts Listener Effort in ALS-related Dysarthria Esteban G. Roitberg, Marcos A. Trevisan, Julian Peller, Diego E. Shalom, Felipe Aguirre, Gastón Bujía, Alan Taitz, Donna Harris, Katie Seaver, Stacey Sullivan, Amy Wright, Jordan R. Green, Jason Osik, Ryan A. Shewcraft, Peng Jiang, Joel Schwartz, Ernest Fraenkel, James D. Berry, Indu Navar Bingham, Lyle W. Ostrow. Background Dysarthria is associated with decreased quality of life in people with ALS. Monitoring progressive changes in speech is challenging due to the complex impact of ALS on multiple speech subsystems. Quantitative measures of dysarthria could be useful as ALS clinical trial outcome measures, providing clinically meaningful insight into the progression of bulbar symptomatology. Listener Effort (LE) is a clinician rated feature, scored from 0-100, describing how much effort a healthy listener needs to exert to understand a dysarthric speaker. Listener Effort is inherently clinically meaningful, can be reliably rated by Speech-Language Pathologists (SLPs) listening to recorded speech samples, changes quantitatively over time in ALS, and is highly reproducible. READ MORE Listener effort measures clinically meaningful change of dysarthria in amyotrophic lateral sclerosis Indu Navar Bingham, Raquel Norel, Esteban G. Roitberg, Julián Peller, Marcos A. Trevisan, Carla Agurto, Michele Merler, Diego E. Shalom, Felipe Aguirre, Iair Embon, Alan Taitz, Donna Harris, Amy Wright, Katie Seaver, Stacey Sullivan, Jordan R. Green, Lyle W. Ostrow, Ernest Fraenkel, James D. Berry Abstract Amyotrophic lateral sclerosis (ALS) is a neurodegenerative motor neuron disease that can cause progressive bulbar dysfunction and dysarthria, resulting in reduced quality of life. Quantitative motor speech analysis can identify features of dysarthria that worsen with ALS progression but are not, inherently, clinically meaningful. Listener effort is a clinician rated feature describing how much effort the listener needs to exert to understand the dysarthric speaker. This study investigated whether listener effort could act as a clinically meaningful measure of ALS dysarthria that could be used as an outcome measure in clinical trials. READ MORE Reliable monitoring of respiratory function with home spirometry in people living with amyotrophic lateral sclerosis Julian Peller, Marcos A. Trevisan, Gaston Bujial, Felipe Aguirrel, Diego E. Shalom, Alan TaitzIt, Stephanie Henzel, Silviya Bastola, Jason Osik, Ryan A. Shewcraft, Peng Jiang, Joel Schwartz, Terry Heiman-Patterson, Michael E. ShermanS, Matthew F. Wipperman, Oren Levy, Guofa Shou, Karl A. Sillay, Lyle W. Ostrow, Ernest Frankel, James D. Berry, Indu Navar Bingham, Esteban G. Roitberg Introduction Monitoring respiratory function is essential for assessing the progression of Amyotrophic Lateral Sclerosis (ALS) and planning interventions. Remote pulmonary function testing offers a promising alternative to in-clinic visits by reducing participant burden and enabling more frequent and accessible measurements. Methods: To evaluate the feasibility and reliability of home-based spirometry in ALS, we built on the Radcliff Study, a fully remote, longitudinal, exploratory study conducted at home by 67 people with ALS (pALS). After an initial training period, participants managed their coaching autonomously, performing spirometry independently or requesting assistance from trained personnel. READ MORE Clinical assessment and interpretation of dysarthria in ALS using attention based deep learning AI models Michele Merler, Carla Agurto, Julian Peller, Esteban Roitberg, Alan Taitz, Marcos A. Trevisan, Indu Navar, James D. Berry, Ernest Fraenkel, Lyle W. Ostrow, Guillermo A. Cecchi and Raquel Norel Abstract Speech dysarthria is a key symptom of neurological conditions like ALS, yet existing AI models designed to analyze it from audio signal rely on handcrafted features with limited inference performance. Deep learning approaches improve accuracy but lack interpretability. We propose an attention-based deep learning AI model to assess dysarthria severity based on listener effort ratings. Using 2,102 recordings from 125 participants, rated by three speech-language pathologists on a 100-point scale, we trained models directly from recordings collected remotely. Our best model achieved R2 of 0.92 and RMSE of 6.78. Attention-based interpretability identified key phonemes, such as vowel sounds influenced by ‘r’ (e.g., “car,” “more”), and isolated inspiration sounds as markers of speech deterioration. This model enhances precision in dysarthria assessment while maintaining clinical interpretability. By improving sensitivity to subtle speech changes, it offers a valuable tool for research and patient care in ALS and other neurological disorders. READ MORE 2024 NEALS Conference A Novel, Self-Administered, App-Based Assessment of Motor Movement in ALS Christina Fournier (Emory University), Indu Navar (EverythingALS), Natalia Luchkina (EverythingALS), Christian Rubio (EverythingALS), and Stephanie Henze (EverythingALS) Abstract This study presents the ALS Motor App, a self-administered, AI-supported tool designed to remotely assess motor movement in individuals with ALS. The app evaluates 46 motor tasks across bulbar, upper extremity, trunk, and lower extremity regions through written descriptions and animated visuals. Users record their ability to perform tasks, with results stored in a central repository for review. Initial beta testing has refined the app using feedback from clinicians and people with ALS (pALS), with the tool now available on Google Play and the Apple Store. The app offers enhanced data granularity and accessibility, supporting adaptive algorithms that track motor decline and predict future care needs. Future work will validate the tool against standardized ALS measures and explore its reliability and predictive power for clinically relevant milestones. READ MORE medRxiv Listener effort quantifies clinically meaningful progression of dysarthria in people living with amyotrophic lateral sclerosis Indu Navar Bingham, Raquel Norel, Esteban G. Roitberg, Julián Peller, Marcos A Trevisan, Carla Agurto, Diego E. Shalom, Felipe Aguirre, Iair Embon, Alan Taitz, Donna Harris, Amy Wright, Katie Seaver, Stacey Sullivan, Jordan R. Green, Lyle W. Ostrow, Ernest Fraenkel, James D. Berry Abstract Amyotrophic lateral sclerosis (ALS) is a neurodegenerative motor neuron disease that causes progressive muscle weakness. Progressive bulbar dysfunction causes dysarthria and thus social isolation, reducing quality of life. The Everything ALS Speech Study obtained longitudinal clinical information and speech recordings from 292 participants. In a subset of 120 participants, we measured speaking rate (SR) and listener effort (LE), a measure of dysarthria severity rated by speech pathologists from recordings. LE intra- and inter-rater reliability was very high (ICC 0.88 to 0.92). LE correlated with other measures of dysarthria at baseline. LE changed over time in participants with ALS (slope 0.77 pts/month; p<0.001) but not controls (slope 0.005 pts/month; p=0.807). The slope of LE progression was similar in all participants with ALS who had bulbar dysfunction at baseline, regardless of ALS site of onset. LE could be a remotely collected clinically meaningful clinical outcome assessment for ALS clinical trials. READ MORE 2024 NEALS Conference Machine Learning Model Predicts Listener Effort in ALS-related Dysarthria Indu Navar (EverythingALS), Esteban G. Roitberg (Universidad Nacional de San Martín and EverythingALS), Julian Peller (Humai and EverythingALS), Marcos A. Trevisan (Universidad de Buenos Aires and CONICET), Diego E. Shalom (Universidad de Buenos Aires and CONICET), Felipe Aguirre (EverythingALS), Gastón Bujía (EverythingALS), Iair Embon (EverythingALS), Alan Taitz (SRI International), Raquel Norel (IBM Research), Carla Agurto (IBM Research), Donna Harris (Temple University), Amy Wright (EverythingALS), Katie Seaver (EverythingALS), Stacey Sullivan (EverythingALS), Jordan R. Green (MGH Institute of Health Professions), Lyle W. Ostrow (Temple University), Ernest Fraenkel (MIT), and James D. Berry (Massachusetts General Hospital and Harvard Medical School) Abstract This study applies machine learning (ML) to predict Listener Effort (LE), a key measure of speech impairment in ALS-related dysarthria. Using 2,124 speech recordings from 125 participants (105 pALS, 20 controls) and manual LE ratings by Speech-Language Pathologists (SLPs) with excellent inter-rater reliability, ML models demonstrated robust predictive capabilities. A simple Lasso regression model achieved an R² of 0.83, with Speaking Rate and Whisper Confidence identified as the two most significant features. Advanced ensemble models achieved even higher accuracy (R² of 0.94). These findings highlight the potential of ML in quantifying LE, offering scalable and reliable tools to track ALS progression and evaluate therapeutic interventions. READ MORE 2024 MND Conference A Novel Web App-Based Assessment of Cognition in ALS Using Speech Indu Navar (EverythingALS), Raquel Norel (IBM), Carla Agurto (IBM), Guillermo A. Cecchi (IBM), Bo Wen (IBM), Natalia Luchkina (EverythingALS), Stephanie Henze (EverythingALS), Alan Taitz (EverythingALS), Ahmad Al Khleifat (King’s College London), James Berry (MGH), Sharon Abrahams (University of Edinburgh), and Ammar Al-Chalabi (King’s College London) Abstract This study introduces a web app-based assessment for evaluating cognition in individuals with ALS, inspired by the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). Data from 108 participants, including people with ALS and controls, were analyzed, with a subset completing repeated evaluations. Speech samples collected through picture description tasks were processed using Whisper Open AI for transcription, extracting acoustic and linguistic features. Linear regression models achieved Spearman correlations between 0.32 and 0.51 for predicting cognitive scores. The results highlight the potential of digitized, speech-based cognitive assessments as scalable, accessible alternatives to traditional methods, especially for individuals in remote or underserved areas. Future work will expand cohort size and refine methodologies to enhance accuracy and generalizability. READ MORE 2024 A Roadmap to Incorporating Digital Endpoints in Clinical Trials 2024-2025 Authors and Contributors EverythingALS Industry Consortia members, EverythingALS Scientific Advisory Board, regulatory advisors, and members of the ALS community, including pALS (people with ALS) and cALS (caregivers of people with ALS). The collaborative effort included input from biopharmaceutical professionals, clinicians, technology developers, and advocacy representatives. The acknowledgment section specifically highlights the ALS community's vital role in shaping the research and insights presented. Objective This white paper advocates for the integration of digital health technologies (DHTs) into ALS clinical trials to enhance efficiency, accessibility, and patient-centricity. Traditional endpoints in ALS trials are burdensome and often lead to high attrition and prolonged durations. By leveraging DHTs, trials can enable continuous, remote, and quantitative patient monitoring, thus reducing bias, improving retention, and broadening accessibility. The roadmap outlined emphasizes interdisciplinary collaboration, agile methodologies, and regulatory alignment to optimize the clinical trial experience for both pALS and cALS. These efforts aim to accelerate innovation, improve disease tracking, and foster a participant-centered research paradigm for ALS care and therapeutics. READ MORE 2024 Harnessing Remote Speech Tasks for Early ALS Biomarker Identification Carla Agurto (IBM), Michele Merler (IBM), Esteban G. Roitberg (EverythingALS), Alan Taitz (formerly EverythingALS, now at SRI International), Marcos A. Trevisan (Universidad de Buenos Aires, CONICET), Diego E. Shalom (Universidad de Buenos Aires, CONICET), Julian Peller (EverythingALS), Lyle W. Ostrow (Temple University), Indu Navar (EverythingALS), Ernest Fraenkel (MIT), James Berry (MGH), Guillermo A. Cecchi (IBM), and Raquel Norel (IBM) Abstract This study investigates acoustic biomarkers for the early detection and monitoring of Amyotrophic Lateral Sclerosis (ALS). Using a dataset of 6,276 speech sessions from 291 participants, including 135 pALS, acoustic features were extracted via OpenSMILE and analyzed with machine learning classifiers. Results show up to 90% AUC in distinguishing ALS stages and 66% AUC for early detection. These findings highlight the potential of speech tasks as biomarkers to improve early diagnosis, track progression, and enhance the understanding of ALS READ MORE ISPOR 2023 Real-World Treatment Preferences Among People Living with ALS: A Discrete Choice Experiment Biogen, Cambridge, MA Trinity Life Sciences, Waltham, MA NEALS Consortium, MA, IBM Research, Yorktown Heights, NY EverythingALS, Seattle, WA Objective Quantitatively assess which treatment attributes are most important to people living with amyotrophic lateral sclerosis (ALS; pALS) in the United States (US) when making treatment decisions. Through direct and indirect assessment of preference, pALS indicated a desire for efficacious treatment options that improve physical functioning and survival. READ MORE 2023 IEEE International Conference on Digital Health (ICDH) Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description Carla Agurto (IBM), Guillermo Cecchi (IBM), Bo Wen (IBM), Ernest Fraenkel (MIT), James Berry (MGH), Indu Navar (EverythingALS) and Raquel Norel (IBM) Abstract In this paper, we focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population. In an effort to reach the ALS population, which frequently exhibits mobility limitations, we implemented the digital version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) test for the first time. READ MORE October 2023 Muscle and Nerve Identifying amyotrophic lateral sclerosis through interactions with an internet search engine Elad Yom-Tov (Microsoft Research) , Indu Navar (EverythingALS), Ernest Fraenkel (MIT) , James D. Berry (MGH) Microsoft Research, Israel EverythingALS, Seattle, WA MIT, Cambridge, MA, MGH, Harvard, MA Abstract We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to 1) 3276 control users and 2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. READ MORE AMIA 2022 Annual Symposium ALS Community Pressing Issues: Lessons from a Survey A. Anvar (EverythingALS), J. Berry (MGH) , E. Fraenkel (MIT), I. Navar (EverythingALS), G. A. Cecchi (IBM), R. Norel (IBM) EverythingALS, Seattle, WA MGH, Cambridge, MA MIT, Harvard, Cambridge, MA IBM Thomas J. Watson Research Center, Yorktown Heights, NY Abstract We gathered survey data to identify the unmet needs expressed by Amyotrophic Lateral Sclerosis (ALS) patients, caregivers, and advocates. Natural Language Processing was used to summarize free text data. Identified needs, named anchor topics were selected manually from the data. Text embedding was used to score participant answers to anchor topics. Despite a broad range of opinions among cohorts, we detected pain control, better access to information and ALSFRS-R alternatives as important ALS community issues. Natural Language Processing (NLP) and Artificial Intelligence (AI) was used to analyze the unstructured text data to obtain a deeper understanding of respondents’ answers. READ MORE Multimodal dialog based speech and facial biomarkers capture differential disease progression rates for ALS remote patient monitoring, M. Neumann, O. Roesler, J. Liscombe, H. Kothare, D. Suendermann-Oeft, J. D. Berry, E. Fraenkel, R. Norel, A. Anvar, I. Navar, A. V. Sherman, J. R. Green and V. Ramanarayanan (2021). In Proc. of: The 32nd International Symposium on Amyotrophic Lateral Sclerosis and Motor Neuron Disease, Virtual, December 2021. Objective Identify audiovisual speech markers that are responsive to clinical progression of Amyotrophic Lateral Sclerosis (ALS). READ MORE Lessons learned from a large-scale audio-visual remote data collection for Amyotrophic Lateral Sclerosis research. Vikram Ramanarayanan, Michael Neumann , Aria Anvar, Oliver Roesler , Jackson Liscombe , Hardik Kothare , David Suendermann-Oeft , James D. Berry , Ernest Fraenkel , Raquel Norel , Alexander V. Sherman, Jordan R. Green and Indu Navar Modality.AI, MGH Institute of Health Professions, Massachusetts Institute of Technology, IBM Thomas J. Watson Research Center, EverythingALS, Peter Cohen Foundation, Harvard University, University of California, San Francisco READ MORE Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale. M. Neumann, O. Roesler, J. Liscombe, H. Kothare, D. Suendermann-Oeft, D. Pautler, I. Navar, A. Anvar, J. Kumm, R. Norel, E. Fraenkel, A. Sherman, J. Berry, G. Pattee, J. Wang, J. Green, V. Ramanarayanan: Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale . Accepted at Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czech Republic, August - September 2021 Accepted at Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czech Republic, August - September 2021. Abstract We investigate the utility of audiovisual dialog systems combined with speech and video analytics for real-time remote monitoring of depression at scale in uncontrolled environment settings. We collected audiovisual conversational data from participants who interacted with a cloud-based multimodal dialog system, and automatically extracted a large set of speech and vision metrics based on the rich existing literature of laboratory studies. We report on the efficacy of various audio and video metrics in differentiating people with mild, moderate and severe depression, and discuss the implications of these results for the deployment of such technologies in real-world neurological diagnosis and monitoring applications. READ MORE Towards A Large-Scale Audio-Visual Corpus for Research on Amyotrophic Lateral Sclerosis A. Anvar, D. Suendermann-Oeft, D. Pautler, V. Ramanarayanan, J. Kumm, J. Berry, R. Norel, E. Fraenkel, and I. Navar: Towards A Large-Scale Audio-Visual Corpus for Research on Amyotrophic Lateral Sclerosis. In Proc. of AAN 2021, 73th Annual Meeting of the American Academy of Neurology, Virtual, April 2021. In Proc. of AAN 2021, 73th Annual Meeting of the American Academy of Neurology, Virtual, April 2021 Objective This presentation describes the creation of a large, open data platform, comprising speech and video recordings of people with ALS and healthy volunteers. Each participant is interviewed by Modality.AI’s virtual agent, emulating the role of a neurologist or speech pathologist walking them through speaking exercises [Fig 1] The collected data is made available to the academic and research community to foster acceleration of the development of biomarkers, diagnostics, therapies, and fundamental scientific understanding of ALS. READ MORE

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Pateint Impact Stories pALS Impact Stories Jan 2023 Montgomery County man with ALS part of study that aims to find root cause of disease "The problem with ALS, unlike many other diseases, is they can't find a biomarker. The biomarkers, what you can see, is affected by a treatment," he described. The former high school public speaking teacher from Montgomery County is one of the 10% with ALS to live more than 10 years. READ MORE Jan 2023 Getting Vocal: Valerie Geerer on Everything ALS research “A friend told me about it, and I was like, ‘Sure, I’ll do it if it will help.’” Once a week, Val connects with an EverythingALS avatar that walks her through several tasks, including saying different words, repeating different phrases, and counting as high as she can in a single breath. READ MORE Tommy Edward Culpepper,Jr., Bentonville man works to help diagnose ALS sooner. Jul 23, 2022 A Bentonville man is dedicating his life to advocating for people with ALS and trying to help diagnose the disease sooner. “I’m a movie connoisseur,” said Tommy Edward Culpepper, Jr. READ MORE Gwen’s story of ALS - These 2 Surprising Traits May Predict ALS, New Research Says. Jul. 20, 2022 How ALS researchers are using an intriguing tool to learn keys of this disease that has at times taken years to diagnose. Plus, one woman's story of why she agreed to participate in this ALS research ( sometimes called Lou Gherig’s disease). READ MORE Groundbreaking research involving artificial intelligence could diagnose ALS patients sooner. Aug 31, 2022 Five thousand people in the U.S. are diagnosed with a neurological disease called ALS every year. Medical experts say it typically takes an average of two years to diagnose, but new research will likely make that timetable a lot shorter. READ MORE Paul Miller, living with ALS for a decade participates in a new ALS research driven by patient-focused non-profit. Jul 28, 2022 New research is underway to better understand and treat ALS, a difficult disease to diagnose with no known cure. ALS patient Paul Miller of Scranton is one of the participants. The progressive neurodegenerative disease ALS affects as many as 30,000 Americans. READ MORE Patients giving voice to ALS research - Brian Andre has been living with ALS for six years. May 31, 2022 Back in 2014, millions of people poured icy water over their heads to spread ALS awareness and support research for the devastating neurodegenerative disease. Now tech developers want to hear your voices. READ MORE Austen Eadie -Friedmann, a Connecticut man is battling a fatal disease with ‘enormous courage’. May 30, 2022 Three years ago Austen Eadie-Friedmann, 38, had a dynamic career in the pharmaceutical/biotech field working for a Fortune 500 company and living in exciting places such as New York City, Boston and Europe, with his husband, William DeGregorio. READ MORE

Alex Young Project Manager Phone: 123-456-7890 Email: info@mysite.com Address: 500 Terry Francine Street San Francisco, CA 94158 Date of Birth: March 14th, 1984 A Bit About Me Everybody has a story, and your visitors would love to hear yours. This space is a great opportunity to give a full background on who you are and what you have to offer at your next job. Double click on the text box to start editing your content and make sure to add all the relevant details you want site visitors to know. Use this space to talk about how you started and share your professional journey. Explain your core values, your commitment to the workplace, and how you stand out from the crowd. Add a photo, gallery, or video for even more engagement. Work Experience June 2025 - April 2026 July 2024 - May 2025 January 2023 - June 2024 This is a Job Description. Briefly describe your specific position, including details about important achievements and milestones. Make sure to include relevant skills and highlights, and don't forget to adjust the timeframe in the subtitle. This is a Job Description. Briefly describe your specific position, including details about important achievements and milestones. Make sure to include relevant skills and highlights, and don't forget to adjust the timeframe in the subtitle. This is a Job Description. Briefly describe your specific position, including details about important achievements and milestones. Make sure to include relevant skills and highlights, and don't forget to adjust the timeframe in the subtitle. Let's Get Social

EverythingALS is a patient-focused non-profit, part of Peter Cohen Foundation (PCF) a 501(3)c organization. Our mission is to support efforts to care for ALS patients and work to find a cure by creating a platform for direct engagement with patients, caregivers, advocates, and researchers. LOGIN > Click here to Register or Login if you are in the study You can help advance ALS Research Join one, or more, of our current studies ALS Austen Study Advancing the Diagnosis and Prognosis of ALS from Speech Our IRB approved study is motivated by the need for early detection and improved prognostic accuracy of ALS using advanced computational technology and speech which includes both audio and video data. Click to Join ALS Gene Carrier Study Families Fight Together This study is focused on early detection of motor and speech changes using emerging digital technologies to support families affected by ALS and FTD. Click to Join ALS Path to Trials Multi-modal study to match you to trial A new IRB approved study that meets you where you are. Whether you can track motor function, speech, or passive data, you'll build a personalized digital baseline so that when the right trial opens, you're already ready to enroll. I’m Interested EverythingALS Radcliff Study Introduction Why Join the Study? Hear from Brian Andre and Vivian Rojas If you are interested in the study, please contact us Your connection to ALS I am interested to participate in * Required The Speech Study to advance drug trials The Radcliff : Multi-disciplinary Study The ALS Gene Carrier Study Are you enrolled in PREVENT ALS Study (DIALS Network at MGH/WashU or ALS Families Study at Columbia University)? Yes No Don't Know For more information click here . Do you use Assistive Devices ? For Walking For Breathing CPAP Other Cane Rollator Walker Wheelchair Other What is your Shoe size? What is your T-shirt size? Women Men Country Submit