International Conference on Rural and Elderly Health Informatics (Togo, W. Africa)

[grow_thumb image=”http://telecareaware.com/wp-content/uploads/2017/05/IREHI.jpg” thumb_width=”150″ /]14-17 December, University of Lomé, Togo, West Africa

The IREHI conference is an annual international conference organized by IEEE International and the University of Lomé. The first meeting will be in Togo and will concentrate on the crisis of care delivery in developing countries, particularly acute in rural areas. The conference will look at how information and communication technologies (ICT), including telehealth and distance care, can improve healthcare. These solutions could significantly contribute to the improvement of health education, diagnosis of diseases, the effectiveness of treatment and monitoring of the elderly, both in urban and rural areas where specialized services are still limited or sometimes non-existent. (more…)

Your temporary tattoo, now with vital signs monitoring!

[grow_thumb image=”http://telecareaware.com/wp-content/uploads/2017/02/graphene-tattoo.jpeg” thumb_width=”150″ /]Can you ever be too rich or too thin? The latter seems to be achievable when it comes to skin patches which can monitor key vital signs like skin temperature, take electroencephalography (EEG) and electromyography (EMG for muscles), and measure hydration. The graphene used in this sensor developed by University of Texas at Austin is 0.3-nm thick, in a polymer 463-nm thick. Unlike Stanford’s stretchy sensor we profiled in November, this doesn’t stretch, but is so thin as to be highly unobtrusive. It is made by growing single-layer graphene on a copper sheet, which is then coated by a stretchy support polymer. The copper is etched off and the polymer-graphene placed on temporary tattoo paper. The wearer doesn’t sense it because, as the researchers termed it, it is compliant with the nooks and crannies of human skin–and it doesn’t look obnoxious. It can be placed on the chest, on the arm or other locations as needed. Testing indicated good quality signals and in fact, detected EKG signals not registering on a conventional monitor. Presented at IEEE’s International Electron Devices Meeting (IEDM). IEEE Spectrum

AI as diagnostician in ophthalmology, dermatology. Faster adoption than IBM Watson?

Three recent articles from the IEEE (formally the Institute of Electronics and Electrical Engineers) Spectrum journal are significant in pointing to advances in artificial intelligence (AI) for specific medical conditions–and which may go into use faster and more cheaply than the massive machine learning/decision support program represented by IBM Watson Health.

A Chinese team developed CC-Cruiser to diagnose congenital cataracts, which affect children and cause irreversible blindness. The program developed algorithms that used a relatively narrow database of 410 images of congenital cataracts and 476 images of normal eyes. The CC-Cruiser team from Sun Yat-Sen and Xidian Universities developed algorithms to diagnose the existence of cataracts, predict the severity of the disease, and suggest treatment decisions. The program was subjected to five tests, with most of the critical ones over 90 percent accuracy versus doctor consults. There, according to researcher and ophthalmologist Haotian Lin, is the ‘rub’–that even with more information, he cannot project the system going to 100 percent accuracy. The other factor is the human one–face to face interaction. He strongly suggests that the CC-Cruiser system is a tool to complement and confirm doctor judgment, and could be used in non-specialized medical centers to diagnose and refer patients. Ophthalmologists vs. AI: It’s a Tie (Hat tip to former TTA Ireland Editor Toni Bunting)

In the diagnosis of skin cancers, a Stanford University team used GoogleNet Inception v3 to build a deep learning algorithm. This used a huge database of 130,000 lesion images from more than 2000 diseases. Inception was successful in performing on par with 21 board-certified dermatologists in differentiating certain skin lesions, for instance, keratinocyte carcinomas from benign seborrheic keratoses. The major limitations here are the human doctor’s ability to touch and feel the skin, which is key to diagnosis, and adding the context of the patient’s history. Even with this, Inception and similar systems could help to triage patients to a doctor faster. Computer Diagnoses Skin Cancers

Contrasting this with IEEE’s writeup on the slow development of IBM Watson Health’s systems, each having to be individually developed, continually refined, using massive datasets, best summarized in Dr Robert Wachter’s remark, “But in terms of a transformative technology that is changing the world, I don’t think anyone would say Watson is doing that today.” The ‘Watson May See You Someday’ article may be from mid-2015, but it’s only this week that Watson for Oncology has announced its first implementation in a regional medical center based in Jupiter, Florida. Watson for Oncology collaborates with Memorial Sloan-Kettering in NYC (MSK) (and was tested in other major academic centers). Currently it is limited to breast, lung, colorectal, cervical, ovarian and gastric cancers, with nine additional cancer types to be added this year. Mobihealthnews

What may change the world of medicine could be AI systems using smaller, specific datasets, with Watson Health for the big and complex diagnoses needing features like natural-language processing.

Gait and balance detector Kinesis QTUG successful in MS assessment study

[grow_thumb image=”http://telecareaware.com/wp-content/uploads/2015/01/QTUG.jpg” thumb_width=”150″ /]TRIL Centre (Dublin, Ireland) spinoff Kinesis, which developed the wearable sensor-based QTUG system for assessing fall risk through measuring gait and balance, was part of a recently presented study of relapsing remitting multiple sclerosis (MS) patients presented at the IEEE International Conference. The QTUG test was used in assessing patient mobility and fall risk over time. The base test, Timed Up and Go (TUG), is manually performed with a timer and observer; the patient rises from a chair, walks three meters, turns around, walks back and then sits back down again. Using this test, the Kinesis sensors reliably assessed the state of patient MS in 21 patients, using 32 of the 52 sensor parameters. In October, according to Mobihealthnews, Kinesis inked a deal with Intel-GE Care Innovations to distribute the system in the US; Intel and GE also are major funders of TRIL. IEEE Xplore abstract (full access on paid site).