search.noResults

search.searching

saml.title
dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
Electronics


Previous page: The University of Surrey’s microscale source- gated and multimodal transistors are designed to achieve high performance at low cost.


It’s an interesting paradigm, apps being used as or in conjunction with a new type of medical device. Emmerich suggests that it will substantially reduce the bill of materials for individual medical devices by removing the need for expensive user interface modules, and allowing them to operate with smaller amounts of memory and lower-spec CPUs. “This combination of low cost, verifiable patient results and major return on investment will attract the attention of both investors and national healthcare systems to apps as medical devices,” he says.


“One of the advantages of our microdevices is the high amplification they achieve, while not requiring particularly high-precision fabrication processes.”


Radu Sporea


Phones might be increasingly central to medical device development – Zühlke, to take one example, is currently investing in sensors to measure all of a person’s vital signs (body temperature, pulse, respiration and blood pressure) through their phone with clinical precision – but they have their own limitations. As small and powerful as they might be, with their abundance of AI features, implanting them into patients is simply not an option. AI has the potential to do even more to change the healthcare paradigm, but the challenge today is the very practical one of finding the space for it.


Small potential


One of the biggest barriers to utilising what AI, machine and deep learning have to offer in the medical device realm is the abundance of resources they require, such as large labelled data set computational capabilities. Today’s AI is too big to put on patient- friendly device hardware and running it on the cloud hurts privacy. That’s a particularly big concern in medicine, says Song Han, assistant professor in electrical engineering and computer science at the Massachusetts Institute of Technology. He and his team are working on what they call tiny machine learning (TinyML) – a way of making AI small enough to run locally on miniature microcontrollers. “We have full-stack innovations from efficient algorithms to systems and hardware,” he explains. The project comprises research into hardware-aware efficient model design techniques and software developments that will help “fully unleash the power of AI on small devices”. Currently, the challenge is that AI algorithms are computationally intensive, power hungry and require a large memory footprint. “When the hardware device shrinks down, the power, computation and memory


74


Innovation in minimisation Radu Sporea, a senior lecturer in semiconductor devices at the University of Surrey, is also working to shrink technologies to enable medical device AI and machine learning. “We are developing new building- blocks and circuits for low power, low-cost electronics,” he says. “Low cost usually means low performance, but we are striving to change that.”


Sporea and his team are creating advanced electronic structures, which can lead to complex functionality even with cheaper materials and fabrication processes, in the form of microscale devices called source-gated transistors and multimodal transistors. “One of the advantages of our microdevices is the high amplification they achieve, while not requiring particularly high-precision fabrication processes,” he says. That means circuits built with these transistors would be good amplifiers for low signals, such as those found in many medical applications. The transistors can also generate and maintain reference signals, and make it easy to realise signal processing with reduced distortion and higher energy efficiency – all without requiring complex


Medical Device Developments / www.nsmedicaldevices.com


budgets become very tight and make it difficult to run advanced AI algorithms,” Song says. To minimise those issues, he’s “co-designing” AI algorithms and hardware platforms for healthcare applications.


“Many workloads in the medical sector can benefit from AI algorithms,” he continues. “The efficient deployment of AI algorithms on small devices will empower smart medical devices.” One of his team’s current projects uses AI to help obtain blood pressure measurements. He believes his work will help make the AI model hardware-aware, meaning it will take the different hardware properties of various medical devices into account as it operates, improving efficiency and potentially compensating for the limitations of current wearable technologies, which often provide unreliable or hard-to-interpret data that cannot be evaluated against an agreed baseline. The work Song’s team is doing is both innovative and complex, incorporating hardware and software to help support enhanced machine learning for an array of applications – not just medical. “Our work can make AI on edge possible, thus opening the door to numerous AI applications and services on the edge,” he proclaims. Intelligent edge devices include smartphones, microcontrollers and other internet of things (IoT) devices running AI algorithms. They can be applied in various scenarios like smart homes, agriculture, factories and medicine, providing services such as conversation, health monitoring, data analysis and real time alerts. “Running AI locally on edge devices also better preserves patients’ privacy,” Song adds.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82  |  Page 83  |  Page 84  |  Page 85  |  Page 86  |  Page 87  |  Page 88  |  Page 89  |  Page 90  |  Page 91  |  Page 92  |  Page 93  |  Page 94  |  Page 95  |  Page 96  |  Page 97  |  Page 98  |  Page 99  |  Page 100  |  Page 101  |  Page 102  |  Page 103  |  Page 104  |  Page 105  |  Page 106  |  Page 107  |  Page 108  |  Page 109  |  Page 110  |  Page 111  |  Page 112  |  Page 113  |  Page 114  |  Page 115  |  Page 116  |  Page 117  |  Page 118  |  Page 119  |  Page 120  |  Page 121  |  Page 122  |  Page 123  |  Page 124  |  Page 125  |  Page 126  |  Page 127  |  Page 128  |  Page 129  |  Page 130  |  Page 131  |  Page 132  |  Page 133