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
Filtration & fluid control


performance. Traditionally, this is achieved with expertise, ingenuity, and trial and error, which makes the design process expensive and challenging.” Helpfully, given enough data, AI algorithms can learn the relationships between design parameters and performance, and support optimal microfluidics design decisions. “That means we don’t need a microfluidic expert present at all times,” adds Lashkaripour. “I have met many brilliant life-science researchers and almost all agree that microfluidics can significantly accelerate the discovery cycle. Researchers want microfluidics but don’t have the necessary expertise, resources or infrastructure. The high barrier to entry is significantly lowered if we have design automation tools.”


Need for speed


Lashkaripour and CIDAR director Douglas Densmore have been working on automating the design of low-cost microfluidic droplet generators, recognising that droplet-based microfluidic devices hold immense potential as inexpensive alternatives to existing screening platforms in areas such as enzyme discovery and early cancer detection. According to WHO, nearly 60% of deaths from breast cancer happen because of a lack of early detection programmes in countries with meagre resources.


“Microfluidics inherently comes with the promise of better data quality in terms of higher sensitivity and accuracy, and faster results with high throughput and quick reaction times,” Lashkaripour remarks. “But the little secret is that developing these devices is extremely slow and expensive. ML models capable of predicting the performance of microfluidic devices can speed up the design process tremendously.” CIDAR has developed a web-based tool, DAFD, that uses ML to predict performance and enable design automation of flow-focusing droplet generators. Lashkaripour hopes it will greatly reduce the need for microfluidic expertise and design iterations. Design automation software could remove inefficiencies, both speeding up the design process and, crucially, making it cheaper. Researchers who are often pursuing a moving target with changing performance requirements may no longer need to start the design process from scratch over and over again. “Changing the geometry means fabricating another device in-house, which takes days and needs a cleanroom, or outsourcing it, which takes several weeks,” adds Lashkaripour. “AI models coupled with automated search algorithms can rapidly suggest designs perfectly tailored to both performance requirements and user design constraints. If the goal is to minimise the sample volume while achieving a prescribed performance, AI models will rapidly test all possible designs in-silico and report the best one possible.”


Commercial break Deepcell, the company founded by Salek and CEO Maddison Masaeli, who invented its AI-based single-cell analysis


84 Medical Device Developments / www.nsmedicaldevices.com


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