Auditory neuropathy spectrum disorder (ANSD) refers to a range of hearing impairments characterized by an impaired transmission of sound from the cochlea to the brain. This defect can be due to a lesion or defect in the inner hair cell (IHC), IHC ribbon synapse (e.g., pre-synaptic release of glutamate), postsynaptic terminals of the spiral ganglion neurons, or demyelination and axonal loss within the auditory nerve. To date, the only clinical treatment options for ANSD are hearing aids and cochlear implantation. However, despite the advances in hearing-aid and cochlear-implant technologies, the quality of perceived sound still cannot match that of the normal ear. Recent advanced genetic diagnostics and clinical audiology made it possible to identify the precise site of a lesion and to characterize the specific disease mechanisms of ANSD, thus bringing renewed hope to the treatment or prevention of auditory neurodegeneration. Moreover, genetic routes involving the replacement or corrective editing of mutant sequences or defected genes to repair damaged cells for the future restoration of hearing in deaf people are showing promise. In this review, we provide an update on recent discoveries in the molecular pathophysiology of genetic lesions, auditory synaptopathy and neuropathy, and gene-therapy research towards hearing restoration in rodent models and in clinical trials.

Background There are many scales for screening the impact of a disease. These scales are generally used to diagnose or assess the type and severity of a disease and are carried out by doctors. The chatbot helps patients suffering from primary headache disorders through personalized text messages. It could be used to collect patient-reported outcomes. Objective The aims of this study were (1) to study whether the collection and analysis of remote scores, without prior medical intervention, are possible by a chatbot, (2) to perform suggested diagnosis and define the type of headaches, and (3) to assess the patient satisfaction and engagement with the chatbot. Method Voluntary users of the chatbot were recruited online. They had to be over 18 and have a personal history of headaches. A questionnaire was presented (1) by text messages to the participants to evaluate migraines (2) based on the criteria of the International Headache Society. Then, the Likert scale (3) was used to assess overall satisfaction with the use of the chatbot. Results We included 610 participants with primary headache disorders. A total of 89.94% (572/610) participants had fully completed the questionnaire (eight items), 4.72% (30/610) had partially completed it, and 5.41% (33) had refused to complete it. Statistical analysis was performed on 86.01% (547/610) of participants. Auto diagnostic showed that 14.26% (78/547) participants had a tension headache, and 85.74% (469/547) had a probable migraine. In this population, 15.78% (74/469) suffered from migraine without probable aura, and 84.22% (395/469) had migraine without aura. The patient's age had a significant incidence regarding the auto diagnosis (P = .008<.05). The evaluation of overall satisfaction shows that a total of 93.9% (599/610) of users were satisfied or very satisfied regarding the timeliness of responses the chatbot provides. Conclusion The study confirmed that it was possible to obtain such a collection remotely, and quickly (average time of 3.24 min) with a high success rate (89.67% (547/610) participants who had fully completed the IHS questionnaire). Users were strongly engaged through chatbot: out of the total number of participants, we observed a very low number of uncompleted questionnaires (6.23% (38/610)). Conversational agents can be used to remotely collect data on the nature of the symptoms of patients suffering from primary headache disorders. These results are promising regarding patient engagement and trust in the chatbot.

According to the World Health Organization, half the adult population around the world suffers from headaches. Even though this condition remains in most cases innocuous, it can have a major impact on the patient's quality of life but also on public health expenditure. Moreover, most patients manage their headaches on their own, without consulting a doctor. Therefore, self-medication can eventually lead to drug overuse, and consequently the emergence of a secondary disease called medication-overuse headache (MOH). The detection and follow-up of these unconventional patients represent a major challenge. Some of the latest technology advancements seem to be tailored and fitting for this context. The goal of this study is to investigate medication overuse in French patients suffering from headaches using the chatbot Vik Migraine. Data collection and analysis were assembled from answers to a questionnaire of 28 questions divided into three parts: socio-demographic profile, drug consumption, and medical follow-up. The study showed that medication overuse was often linked to increased headache frequency. Prescription drugs like triptans and opioids, were the most overused drugs among the cohort. This suggests that healthcare professionals could play a critical role in targeting these drugs in prevention of overuse.

En réponse à la crise sanitaire causée par le Sars-CoV-2, de nombreux pays ont pris la décision de confiner leur population. Il a été démontré que des événements traumatisants peuvent être responsables d'altérations de la santé telles qu'une détresse péritraumatique ou un syndrome de stress post-traumatique. Cependant, l'étendue des effets de la pandémie et des confinements sur la santé mentale demeurent encore mal connue. L'Objectif de cette étude était d'évaluer chez des patients à risque la détresse péritraumatique en France, où la propagation de l'épidémie a imposé un confinement généralisé. Des patients ont été recrutés parmi les utilisateurs de quatre chatbots dédiés respectivement au cancer du sein, à l'asthme, à la dépression et à la migraine. La détresse psychologique des patients a été mesurée en utilisant l'inventaire de détresse péritraumatique (PDI), une échelle de référence validée. Une corrélation a ensuite été effectuée entre le risque de détresse péritraumatique et différentes caractéristiques des patients. Au total, 1771 participants ont été inclus. Les femmes représentaient 91,25 % (n = 1616) avec une moyenne d'âge de 33 ans et 7,96 % (n = 141) étaient des hommes avec une moyenne d'âge de 28 ans (Tableau 1). Le score PDI médian était de 10 chez les patientes atteintes de cancer du sein, 11 chez les patients atteints de migraine, 12 chez les patients asthmatiques et 13 chez les patients dépressifs (Fig. 1). Un total de 38,06 % (n = 674) des patients étaient en détresse psychologique (score PDI ≥ 14), définie comme un état de risque accru de syndrome de stress post-traumatique. La prévalence de détresse psychologique était de 42 % chez les patients asthmatiques (209/497), 34 % chez les patientes d'un cancer du sein (123/360), 54 % chez les patients dépressifs (246/459) et 39 % chez les patients migraineux (178/455). Une analyse de variance a permis de montrer que les femmes, la dépression et le non-emploi étaient associés à des scores PDI significativement plus élevés. Chez ces patients à risque, on observe à la fois des émotions dysphoriques et la perception d'un risque vital. Les patients qui utilisent leur smartphone ou leur ordinateur pendant plus d'une heure par jour avaient également des scores PDI significativement plus élevés (p = 0,026). Cette étude a permis de montrer le très fort impact de la COVID-19 sur la santé mentale chez plus de 1700 patients. Certaines catégories de patients, telles que les femmes, les personnes sans emploi ou dépressives, apparaissent plus vulnérables face au risque de troubles psychologiques causés par la pandémie.

There are many scales for screening or assessing the impact of a disease. These scales are generally used to diagnose or assess the severity of a disease and are carried out by doctors. The Vik Migraine chatbot helps patients suffering from headaches through personalized text messages, it could be used to collect patient-reported outcomes. The aims of this study were (1) to assess the feasibility of collecting a chatbot-mediated reference scale, (2) perform a remote diagnosis of the severity of the migraines and (3) assess the patient satisfaction and engagement with the chatbot. This study was conducted in France from December 2019 to March 2020. Voluntary users of the chatbot Vik Migraine were recruited online. They had to be adults and suffer from chronic migraines. An adapted version of the IHS questionnaire was presented to the participants by text messages. The Lickert scale ranging from 1 to 5 was used to assess overall satisfaction with the use of Vik Migraine. We included 636 participants with migraines or headaches. A total of 89.94% (572) participants had fully completed the IHS questionnaire (8 items), 4.72% (30) had partially completed it and 5.35% (34) had refused to complete it. The evaluation of overall satisfaction shows that a total of 80.7% (513) of users agreed or strongly agreed with the affirmation that Vik Migraine provides quality answers about the pathology or its information. We hypothesized that a virtual assistant built to support migraine patients could be used to retrieve patient data remotely, such as medical assessment scales. The study confirmed this hypothesis and showed that users were strongly engaged through to chatbot: out of the total number of participants, we observed a very low number of uncompleted questionnaires.

BACKGROUND:Lockdowns were implemented to limit the spread of COVID-19. Peritraumatic distress (PD) and post-traumatic stress disorder have been reported after traumatic events, but the specific effect of the pandemic is not well known.AIM:The aim of this study was to assess PD in France, a country where COVID-19 had such a dramatic impact that it required a country-wide lockdown.METHODS:We recruited patients in four groups of chatbot users followed for breast cancer, asthma, depression and migraine. We used the Psychological Distress Inventory (PDI), a validated scale to measure PD during traumatic events, and correlated PD risk with patients' characteristics in order to better identify the ones who were the most at risk.RESULTS:The study included 1771 participants. 91.25% (n=1616) were female with a mean age of 32.8 (13.71) years and 7.96% (n=141) were male with a mean age of 28.0 (8.14) years. In total, 38.06% (n=674) of the respondents had psychological distress (PDI ≥14). An analysis of variance showed that unemployment and depression were significantly associated with a higher PDI score. Patients using their smartphones or computers for more than 1 hour a day also had a higher PDI score (p=0.026).CONCLUSION:Prevalence of PD in at-risk patients is high. These patients are also at an increased risk of developing post-traumatic stress disorder. Specific steps should be implemented to monitor and prevent PD through dedicated mental health policies if we want to limit the public health impact of COVID-19 in time.TRIAL REGISTRATION NUMBER:NCT04337047.

Benjamin Chaix*1-3, Arthur Guillemassé3, Pierre Nectoux3, Guillaume Delamon3, Jean Emmanuel Bibault4 and Benoît Brouard3 Author Affiliations 1ENT & Head and Neck Surgery Department, Hôpital Gui de Chauliac, Montpellier, France 2Université Montpellier 1, France 3WeFight, Institut du Cerveau et de la Moelle épinière, Hôpital Pitié-Salpêtrière, France 4Department of Radiation Oncology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France Received: April 06, 2020 | Published: April 20, 2020 Corresponding author: Benjamin Chaix, Département d’ORL, CHRU Gui de Chauliac 80 avenue Augustin Fliche, 34264 Montpellier, France DOI: 10.26717/BJSTR.2020.27.004453

Background: Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers.In the field of health, chatbots can improve patient information, monitoring, or treatment adherence.Method: The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe.Results: Wefight designed a chatbot named Vik to empower patients with cancers or chronic diseases and their relatives via personalized text messages.Natural Language Processing models were used.We built several Vik for each disease.Each Vik has its contents, its own NLP model and interacts its way with the patient.Conclusion: Conversational agents may help patients with minor health concerns without seeing a real physician.If the quality of these softwares is not thoroughly assessed, they could be dangerous.If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence.

Introduction More than 2.5 billion people in the world are currently in lockdowns to limit the spread of the novel coronavirus disease 2019 (COVID-19). Psychological Distress (PD) and Post-Traumatic Stress Disorder have been reported after traumatic events, but the specific effect of pandemics is not well known. The aim of this study was to assess PD in France, a country where COVID-19 had such a dramatic impact that it required a country-wide lockdown. Patients and methods We recruited patients in 4 groups of chatbot users followed for breast cancer, asthma, depression and migraine. We used the Psychological Distress Index (PDI), a validated scale to measure PD during traumatic events, and correlated PD risk with patients characteristics in order to better identify the one who were the most at-risk. Results The study included 1771 participants. 91.25% (1616) were female with a mean age of 32.8 years (SD=13,71), 7.96% (141) were male with a mean age of 28.0 years (SD=8,14). In total, 38.06% (674) of the respondents had psychological distress (PDI ≥15). An ANOVA analysis showed that sex (p=0.00132), unemployment (p=7.16x10-6) and depression (p=7.49x10-7) were significantly associated with a higher PDI score. Patients using their smartphone or computer more than one hour a day also had a higher PDI score (p=0.02588). Conclusion Prevalence of PD in at-risk patients is high. These patients are also at increased risk to develop Post-Traumatic Stress Disorder. Specific steps should be implemented to monitor and prevent PD through dedicated mental health policies if we want to limit the public health impact of COVID-19 in time.

Objectif : Le chatbot est un logiciel qui utilise l’apprentissage statistique et a pour objectif de simuler une conversation par message textuel ou vocal. Le chatbot Vik a été développé dans le but d’améliorer la qualité de vie des patients atteints d’un cancer ou d’une maladie chronique. L’objectif de cette étude pilote est de mesurer l’humeur de patients atteints d’un cancer du sein, avant et après accompagnement par le chatbot Vik. Matériel et méthodes : Les patients ont été recrutés lors de la première utilisation de Vik. Ils ont été triés en fonction des critères d’inclusion (âge > 18 ans, atteints d’un cancer du sein et en cours de traitement, non-opposition, connaissance d’Internet). Ils ne devaient pas être suivis pour des troubles dépressifs ou en cours d’une psychothérapie. Le questionnaire PHQ-9 a été utilisé pour l’évaluation des symptômes de dépression. Seuls les patients ayant un score supérieur à 5 étaient inclus dans l’étude (j0). Le PHQ-9 était ensuite reproposé à j+15 puis à j+30. Résultats : Les utilisateurs recrutés (n = 74) avaient entre 26 et 78 ans. La moyenne d’âge était de 50 ans. Le taux de satisfaction globale était de 94 %. Le score moyen obtenu au PHQ-9 avant utilisation du chatbot (j0) était de 9,73 (ET : 2,02). À l’issue des 30 jours de l’expérimentation, il était de 5,00 (ET : 2,82). L’évolution de l’humeur au cours du temps était croissante à mesure que les participants discutaient avec le chatbot. Conclusion : Cette étude apporte des éléments prometteurs sur la possibilité d’un assistant virtuel conçu pour soutenir les patients d’offrir une méthode attrayante de suivi et de complémentarité aux méthodes traditionnelles de thérapie.

Aims: Chatbot is a software that uses machine learning and aims to simulate a conversation by text or voice message. The Vik chatbot was developed to improve the quality of life of patients with cancer or chronic disease. The objective of this pilot study is to measure the mood of breast cancer patients before and after being accompanied by the Vik chatbot. Procedure: Patients were recruited when they first used Vik. They were filtered according to inclusion criteria (age > 18 years, breast cancer patients, under treatment, non-opposition, internet knowledge). They were not to be followed for depressive disorders or undergoing psychotherapy. The PHQ-9 questionnaire was used to assess symptoms of depression. The patients with a score greater than 5 were only included in the study (JO). The PHQ-9 was then re-proposed at d+15 and d+30. Results: Users recruited (N= 74) were between 26 and 78 years of age. The average age was 50 years. The overall satisfaction rate was 94%. The average score obtained at PHQ-9 before using the chatbot (d0) was 9.73 (SD: 2.02). At the end of the 30 days of the experiment it was 5.00 (SD: 2.82). The change in mood over time has increased as participants chatted with the chatbot. Conclusion: This study provides promising evidence that a virtual assistant designed to support patients has the potential to offer an attractive method of follow-up and could be complementary to traditional methods of therapy.

Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers...

Background A chatbot is a software that interacts with users by simulating a human conversation through text or voice via smartphones or computers. It could be a solution to follow up with patients during their disease while saving time for health care providers. Objective The aim of this study was to evaluate one year of conversations between patients with breast cancer and a chatbot. Methods Wefight Inc designed a chatbot (Vik) to empower patients with breast cancer and their relatives. Vik responds to the fears and concerns of patients with breast cancer using personalized insights through text messages. We conducted a prospective study by analyzing the users’ and patients’ data, their usage duration, their interest in the various educational contents proposed, and their level of interactivity. Patients were women with breast cancer or under remission. Results A total of 4737 patients were included. Results showed that an average of 132,970 messages exchanged per month was observed between patients and the chatbot, Vik. Thus, we calculated the average medication adherence rate over 4 weeks by using a prescription reminder function, and we showed that the more the patients used the chatbot, the more adherent they were. Patients regularly left positive comments and recommended Vik to their friends. The overall satisfaction was 93.95% (900/958). When asked what Vik meant to them and what Vik brought them, 88.00% (943/958) said that Vik provided them with support and helped them track their treatment effectively. Conclusions We demonstrated that it is possible to obtain support through a chatbot since Vik improved the medication adherence rate of patients with breast cancer.

Conversational agents are computer software capable of having a natural conversation. They are increasingly used in healthcare, but research in this field is very scarce. . If chatbots are to be safely used by a large number of patients, they must be evaluated like a medical device, or even a drug.

Background The data regarding the use of conversational agents in oncology are scarce. Objective The aim of this study was to verify whether an artificial conversational agent was able to provide answers to patients with breast cancer with a level of satisfaction similar to the answers given by a group of physicians. Methods This study is a blind, noninferiority randomized controlled trial that compared the information given by the chatbot, Vik, with that given by a multidisciplinary group of physicians to patients with breast cancer. Patients were women with breast cancer in treatment or in remission. The European Organisation for Research and Treatment of Cancer Quality of Life Group information questionnaire (EORTC QLQ-INFO25) was adapted and used to compare the quality of the information provided to patients by the physician or the chatbot. The primary outcome was to show that the answers given by the Vik chatbot to common questions asked by patients with breast cancer about their therapy management are at least as satisfying as answers given by a multidisciplinary medical committee by comparing the success rate in each group (defined by a score above 3). The secondary objective was to compare the average scores obtained by the chatbot and physicians for each INFO25 item. Results A total of 142 patients were included and randomized into two groups of 71. They were all female with a mean age of 42 years (SD 19). The success rates (as defined by a score >3) was 69% (49/71) in the chatbot group versus 64% (46/71) in the physicians group. The binomial test showed the noninferiority (P<.001) of the chatbot’s answers. Conclusions This is the first study that assessed an artificial conversational agent used to inform patients with cancer. The EORTC INFO25 scores from the chatbot were found to be noninferior to the scores of the physicians. Artificial conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. Trial Registration Clinicaltrials.gov NCT03556813, https://tinyurl.com/rgtlehq

The quantities of data generated by the different digital tools currently available are ever greater. In medicine, this information can now be identified and used in predictive modelling. It is also possible to assess methods and medicines during a trial more or less in real time and especially in the patient's daily life, in 'real life'. (c) 2018 Elsevier Masson SAS. All rights reserved

Dominant optic atrophy is a rare inherited optic nerve degeneration caused by mutations in the mitochondrial fusion gene OPA1. Recently, the clinical spectrum of dominant optic atrophy has been extended to frequent syndromic forms, exhibiting various degrees of neurological and muscle impairments frequently found in mitochondrial diseases. Although characterized by a specific loss of retinal ganglion cells, the pathophysiology of dominant optic atrophy is still poorly understood. We generated an Opa1 mouse model carrying the recurrent Opa1(delTTAG) mutation, which is found in 30% of all patients with dominant optic atrophy. We show that this mouse displays a multi-systemic poly-degenerative phenotype, with a presentation associating signs of visual failure, deafness, encephalomyopathy, peripheral neuropathy, ataxia and cardiomyopathy. Moreover, we found premature age-related axonal and myelin degenerations, increased autophagy and mitophagy and mitochondrial supercomplex instability preceding degeneration and cell death. Thus, these results support the concept that Opa1 protects against neuronal degeneration and opens new perspectives for the exploration and the treatment of mitochondrial diseases.