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TitleApplication of mobile health, telemedicine and artificial intelligence to echocardiography.
AuthorsSeetharam, K; Kagiyama, N; Sengupta, PP
JournalEcho research and practice
Publication Date1 Jun 2019
Date Added to PubMed8 Mar 2019
AbstractThe intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.
TitleTelemedicine, Telestroke, and Artificial Intelligence Can Be Coded with the International Classification of Health Interventions.
AuthorsOhannessian, R; Fortune, N; Moulin, T; Madden, R
JournalTelemedicine journal and e-health : the official journal of the American Telemedicine Association
Publication Date1 May 2020
Date Added to PubMed18 Jul 2019
TitleThe Real Era of the Art of Medicine Begins with Artificial Intelligence.
AuthorsMeskó, B
JournalJournal of medical Internet research
Publication Date18 Nov 2019
Date Added to PubMed19 Nov 2019
AbstractPhysicians have been performing the art of medicine for hundreds of years, and since the ancient era, patients have turned to physicians for help, advice, and cures. When the fathers of medicine started writing down their experience, knowledge, and observations, treating medical conditions became a structured process, with textbooks and professors sharing their methods over generations. After evidence-based medicine was established as the new form of medical science, the art and science of medicine had to be connected. As a result, by the end of the 20th century, health care had become highly dependent on technology. From electronic medical records, telemedicine, three-dimensional printing, algorithms, and sensors, technology has started to influence medical decisions and the lives of patients. While digital health technologies might be considered a threat to the art of medicine, I argue that advanced technologies, such as artificial intelligence, will initiate the real era of the art of medicine. Through the use of reinforcement learning, artificial intelligence could become the stethoscope of the 21st century. If we embrace these tools, the real art of medicine will begin now with the era of artificial intelligence.
TitleArtificial intelligence for diabetic retinopathy screening, prediction and management.
AuthorsGunasekeran, DV; Ting, DSW; Tan, GSW; Wong, TY
JournalCurrent opinion in ophthalmology
Publication Date1 Sep 2020
Date Added to PubMed3 Aug 2020
AbstractDiabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management.
TitlePreliminary Evaluation of a mHealth Coaching Conversational Artificial Intelligence for the Self-Care Management of People with Sickle-Cell Disease.
AuthorsIssom, DZ; Rochat, J; Hartvigsen, G; Lovis, C
JournalStudies in health technology and informatics
Publication Date16 Jun 2020
Date Added to PubMed24 Jun 2020
AbstractAdherence to the complex set of recommended self-care practices among people with Sickle-Cell Disease (SCD) positively impacts health outcomes. However, few patients possess the required skills (i.e. disease-specific knowledge, adequate levels of self-efficacy). Consequently, adherence rates remain low and only 1% of patients are empowered enough to master the self-care practices. Health coaching and therapeutic patient education have emerged as new approaches to enhance patients' self-management and support health behavior changes. This preliminary feasibility study examined patients' perceived usefulness of the information provided by a chatbot we developed following patient-important requirements collected during our preliminary studies. Participants tested the chatbot and completed a post-test survey. A total of 19 patients were enrolled and 2 withdrew. 15 respondents (15/17, 88%) gave a score of at least 3/4 to the question "The chatbot contains all the information I need". Results suggest that mHealth coaching apps could be used to promote the knowledge acquisition of recommended health behaviors related to the prevention of SCD main symptoms.
TitleConvolutional neural networks for wound detection: the role of artificial intelligence in wound care.
AuthorsOhura, N; Mitsuno, R; Sakisaka, M; Terabe, Y; Morishige, Y; Uchiyama, A; Okoshi, T; Shinji, I; Takushima, A
JournalJournal of wound care
Publication Date1 Oct 2019
Date Added to PubMed11 Oct 2019
AbstractTelemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessment of telemedicine (MAST) has been emphasised. The development of an eHealth-supported wound assessment system using artificial intelligence is awaited. This study explored whether or not wound segmentation of a diabetic foot ulcer (DFU) and a venous leg ulcer (VLU) by a convolutional neural network (CNN) was possible after being educated using sacral pressure ulcer (PU) data sets, and which CNN architecture was superior at segmentation. CNNs with different algorithms and architectures were prepared. The four architectures were SegNet, LinkNet, U-Net and U-Net with the VGG16 Encoder Pre-Trained on ImageNet (Unet_VGG16). Each CNN learned the supervised data of sacral pressure ulcers (PUs). Among the four architectures, the best results were obtained with U-Net. U-Net demonstrated the second-highest accuracy in terms of the area under the curve (0.997) and a high specificity (0.943) and sensitivity (0.993), with the highest values obtained with Unet_VGG16. U-Net was also considered to be the most practical architecture and superior to the others in that the segmentation speed was faster than that of Unet_VGG16. The U-Net CNN constructed using appropriately supervised data was capable of segmentation with high accuracy. These findings suggest that eHealth wound assessment using CNNs will be of practical use in the future.
TitleCombining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.
AuthorsBerrouiguet, S; Barrigón, ML; Castroman, JL; Courtet, P; Artés-Rodríguez, A; Baca-García, E
JournalBMC psychiatry
Publication Date7 Sep 2019
Date Added to PubMed9 Sep 2019
AbstractThe screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone's native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients' data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants' daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patient. NCT03720730. Retrospectively registered.
TitleeHealth and Clinical Documentation Systems.
AuthorsKnaup, P; Benning, NH; Seitz, MW; Eisenmann, U
JournalStudies in health technology and informatics
Publication Date25 Sep 2020
Date Added to PubMed30 Sep 2020
AbstracteHealth is the use of modern information and communication technology (ICT) for trans-institutional healthcare purposes. Important subtopics of eHealth are health data sharing and telemedicine. Most of the clinical documentation to be shared is collected in patient records to support patient care. More sophisticated approaches to electronic patient records are trans-institutional or (inter-)national. Other aims for clinical documentation are quality management, reimbursement, legal issues, and medical research. Basic prerequisite for eHealth is interoperability, which can be divided into technical, semantic and process interoperability. There is a variety of international standards to support interoperability. Telemedicine is a subtopic of eHealth, which bridges spatial distance by using ICT for medical (inter-)actions. We distinguish telemedicine among healthcare professionals and telemedicine between health care professionals and patients. Both have a great potential to face the challenges of aging societies, the increasing number of chronically ill patients, multimorbidity and low number of physicians in remote areas. With ongoing digitalization more and more data are available digitally. Clinical documentation is an important source for big data analysis and artificial intelligence. The patient has an important role: Telemonitoring, wearable technologies, and smart home devices provide digital health data from daily life. These are high-quality data which can be used for medical decisions.
TitleTele-robotics and artificial-intelligence in stroke care.
AuthorsRabinovich, EP; Capek, S; Kumar, JS; Park, MS
JournalJournal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Publication Date1 Sep 2020
Date Added to PubMed20 Oct 2020
AbstractIn the last forty years, the field of medicine has experienced dramatic shifts in technology-enhanced surgical procedures - from its initial use in 1985 for neurosurgical biopsies to current implementation of systems such as magnetic-guided catheters for endovascular procedures. Systems such as the Niobe Magnetic Navigation system and CorPath GRX have allowed for utilization of a fully integrated surgical robotic systems for perioperative manipulation, as well as tele-controlled manipulation systems for telemedicine. These robotic systems hold tremendous potential for future implementation in cerebrovascular procedures, but lack of relevant clinical experience and uncharted ethical and legal territory for real-life tele-robotics have stalled their adoption for neurovascular surgery, and might present significant challenges for future development and widespread implementation. Yet, the promise that these technologies hold for dramatically improving the quality and accessibility of cerebrovascular procedures such as thrombectomy for acute stroke, drives the research and development of surgical robotics. These technologies, coupled with artificial intelligence (AI) capabilities such as machine learning, deep-learning, and outcome-based analyses and modifications, have the capability to uncover new dimensions within the realm of cerebrovascular surgery.
TitleThe Role of Telemedicine, In-Home Testing and Artificial Intelligence to Alleviate an Increasingly Burdened Healthcare System: Diabetic Retinopathy.
AuthorsPieczynski, J; Kuklo, P; Grzybowski, A
JournalOphthalmology and therapy
Publication Date1 Sep 2021
Date Added to PubMed23 Jun 2021
AbstractIn the presence of the ever-increasing incidence of diabetes mellitus (DM), the prevalence of diabetic eye disease (DED) is also growing. Despite many improvements in diabetic care, DM remains a leading cause of visual impairment in working-age patients. So far, prevention has been the best way to protect vision. The sooner we diagnose DED, the more effective the treatment is. Thus, diabetic retinopathy (DR) screening, especially with imaging techniques, is a method of choice for vision protection. To alleviate the burden of diabetic patients who need ophthalmic care, telemedicine and in-home testing are used, supported by artificial intelligence (AI) algorithms. This is why we decided to evaluate current image teleophthalmology methods used for DR screening. We searched the PubMed platform for papers published over the last 5 years (2015-2020) using the following key words: telemedicine in diabetic retinopathy screening, diabetic retinopathy screening, automated diabetic retinopathy screening, artificial intelligence in diabetic retinopathy screening, smartphone diabetic retinopathy testing. We have included 118 original articles meeting the above criteria, discussing imaging diabetic retinopathy screening methods. We have found that fundus cameras, stable or mobile, are most commonly used for retinal photography, with portable fundus cameras also relatively common. Other possibilities involve the use of ultra-wide-field (UWF) imaging and even optical coherence tomography (OCT) devices for DR screening. Also, the role of smartphones is increasingly recognized in the field. Retinal fundus images are assessed by humans instantly or remotely, while AI algorithms seem to be useful tools facilitating retinal image assessment. The common use of smartphones and availability of relatively cheap, easy-to-use adapters for retinal photographs augmented by AI algorithms make it possible for eye fundus photographs to be taken by non-specialists and in non-medical setting. This opens the way for in-home testing conducted on a much larger scale in the future. In conclusion, based on current DR screening techniques, we can suggest that the future practice of eye care specialists will be widely supported by AI algorithms, and this way will be more effective.
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