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Showing posts with label artificial. Show all posts
Showing posts with label artificial. Show all posts

Friday, July 6, 2012

Is summertime bringing new wave of ads for artificial knees?

I was watching the NBC Nightly News the other night and saw a Stryker ad for its GetAroundKnee.com.  I couldn’t find the TV commercial online, but this is from their website:

That same day, I found this ad in the July issue of Prevention magazine.

Together, hip and knee replacement surgeries already represent the largest hospital expense for Medicare. And, according to an article in Time magazine, the money spent on these two procedures is expected to reach $65.2 billion by 2015.

There is no doubt that part of Medicare reform will involve looking at ways to reduce this cost. One approach is to move the choice of device away from vague “physician preference” and toward evidence-based criteria. The goal will be to use comparative-effectiveness studies to identify which implants are the best-performing, longest-lasting and most cost-effective devices. Many countries have established national registries for hip and knee implant surgeries that include a record of each surgery, the type of device used and reports of complications. Such a registry would improve patient safety and quality of care, according to a report by Kaiser Permanente researchers that was published in November in the Journal of Bone and Joint Surgery. It would make it easier to counsel patients, identify risk factors, track implanted devices during recalls and assess comparative effectiveness of devices, according to lead author Elizabeth Paxton, director of surgical outcomes and analysis at Kaiser.

The American Joint Replacement Registry was created recently, and just this January began a pilot project collecting hip and knee replacement information from 16 representative hospitals. In a statement, the organization (made up of surgeons, executives from the device industry, payers and patient representatives) said that its “long-term goal is to capture data from 90 percent of U.S. hospitals where hip and knee arthroplasty procedures are performed, which amounts to between 5,000 and 6,000 different hospitals, in the next 5 years.”

In the end, marketing devices directly to consumers is antithetical to these other measures that are designed to promote evidence-based treatments. One argument that drug companies have always made to support their (direct-to-consumer) DTC ads is that they are “educational” for consumers. And there may in fact be men in their 40’s or 50’s with degenerative hip disease or other painful, disabling condition that learn about hip resurfacing from a TV ad. Maybe they find out that they don’t have to spend 15 more years disabled as they wait for a total hip replacement. These newly educated fellows may then go to an orthopedic surgeon who (with no conflict of interest) helps them decide whether this is the right approach for them. That is educational.

But DTC ads cast a very wide net. And they work to draw in a wide customer base, raising expectations and brushing over risks and cheaper options. If they didn’t do this, companies like Smith & Nephew wouldn’t spend millions running them. Unless insurers—both public and private—start using evidence-based decision making to set coverage for new hip implant devices, the number of younger patients undergoing more expensive procedures will likely rise—sometimes for the wrong reasons.

We’ll continue to watch.


View the original article here

Is summertime bringing new wave of ads for artificial knees?

I was watching the NBC Nightly News the other night and saw a Stryker ad for its GetAroundKnee.com.  I couldn’t find the TV commercial online, but this is from their website:

That same day, I found this ad in the July issue of Prevention magazine.

Together, hip and knee replacement surgeries already represent the largest hospital expense for Medicare. And, according to an article in Time magazine, the money spent on these two procedures is expected to reach $65.2 billion by 2015.

There is no doubt that part of Medicare reform will involve looking at ways to reduce this cost. One approach is to move the choice of device away from vague “physician preference” and toward evidence-based criteria. The goal will be to use comparative-effectiveness studies to identify which implants are the best-performing, longest-lasting and most cost-effective devices. Many countries have established national registries for hip and knee implant surgeries that include a record of each surgery, the type of device used and reports of complications. Such a registry would improve patient safety and quality of care, according to a report by Kaiser Permanente researchers that was published in November in the Journal of Bone and Joint Surgery. It would make it easier to counsel patients, identify risk factors, track implanted devices during recalls and assess comparative effectiveness of devices, according to lead author Elizabeth Paxton, director of surgical outcomes and analysis at Kaiser.

The American Joint Replacement Registry was created recently, and just this January began a pilot project collecting hip and knee replacement information from 16 representative hospitals. In a statement, the organization (made up of surgeons, executives from the device industry, payers and patient representatives) said that its “long-term goal is to capture data from 90 percent of U.S. hospitals where hip and knee arthroplasty procedures are performed, which amounts to between 5,000 and 6,000 different hospitals, in the next 5 years.”

In the end, marketing devices directly to consumers is antithetical to these other measures that are designed to promote evidence-based treatments. One argument that drug companies have always made to support their (direct-to-consumer) DTC ads is that they are “educational” for consumers. And there may in fact be men in their 40’s or 50’s with degenerative hip disease or other painful, disabling condition that learn about hip resurfacing from a TV ad. Maybe they find out that they don’t have to spend 15 more years disabled as they wait for a total hip replacement. These newly educated fellows may then go to an orthopedic surgeon who (with no conflict of interest) helps them decide whether this is the right approach for them. That is educational.

But DTC ads cast a very wide net. And they work to draw in a wide customer base, raising expectations and brushing over risks and cheaper options. If they didn’t do this, companies like Smith & Nephew wouldn’t spend millions running them. Unless insurers—both public and private—start using evidence-based decision making to set coverage for new hip implant devices, the number of younger patients undergoing more expensive procedures will likely rise—sometimes for the wrong reasons.

We’ll continue to watch.


View the original article here

Thursday, June 14, 2012

Sleep scoring using artificial neural networks

Marina Ronzhinaa, Corresponding author contact information, E-mail the corresponding author, Oto Janoušeka, d, E-mail the corresponding author, Jana Kolárováa, e, E-mail the corresponding author, Marie Novákováb, g, E-mail the corresponding author, Petr Honzíkc, h, E-mail the corresponding author, Ivo Provazníka, f, E-mail the corresponding authora Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech Republicb Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, Brno 62500, Czech Republicc Department of Control and Instrumentation, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech RepublicReceived 18 March 2011. Revised 30 June 2011. Accepted 30 June 2011. Available online 24 October 2011.View full text Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved – next to other classification methods – by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

prs.rt("abs_end");Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks

Figures and tables from this article:

Fig. 1. Schematic representation. (a) Single neuron with vector input. (b) One-layer network with m neurons.

View Within ArticleFig. 2. Transfer functions. (a) Log-sigmoid. (b) Tan-sigmoid. (c) Hard limit. (d) Linear.

View Within ArticleFig. 3. Extraction of the 4-elements features vector from EEG epoch. PSD – power spectral density, d, ?, a, ß – delta, theta, alpha and beta bands, respectively, drel, ?rel, arel, ßrel – relative power values for delta, theta, alpha and beta bands, respectively.

View Within ArticleTable 1. Summary of artificial neural network (ANN) based systems for sleep scoring. BP: backpropagation, EEG: electroencephalogram, EMG: electromyogram, EOG: electrooculogram (LEOG, REOG: left, right EOG, respectively), FC: fully connected, FT: Fourier transform, MLNN: multilayer neural network, MLP: multilayer perceptron, MT: movement time, RatP: ratio power, REM, rapid eye movement, RMS: root mean square, RP: relative power, RUM, LM: Rumelhart (gradient descent without momentum) and Levenberge-Marquardt learning algorithm, respectively, S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, SD: standard deviation, SOM: self-organizing map, SWS: slow wave sleep, TP: total power, W: wakefulness, WT: wavelet transform. Description of sleep stages is according to R&K and AASM.

View table in articleView Within ArticleTable 2. Output neurons of proposed artificial neural network models. REM: rapid eye movement, S*: stage involving four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View table in articleView Within ArticleTable 3. Results of sleep scoring obtained by proposed artificial neural network (ANN) models. EEG: electroencephalogram, REM: rapid eye movement, RP, relative power, S: stage involving the four stages of non-REM and REM sleep, S*: stage involving the four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View table in articleView Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

prs.rt('data_end');

View the original article here

Sleep scoring using artificial neural networks

Marina Ronzhinaa, Corresponding author contact information, E-mail the corresponding author, Oto Janoušeka, d, E-mail the corresponding author, Jana Kolárováa, e, E-mail the corresponding author, Marie Novákováb, g, E-mail the corresponding author, Petr Honzíkc, h, E-mail the corresponding author, Ivo Provazníka, f, E-mail the corresponding authora Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech Republicb Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, Brno 62500, Czech Republicc Department of Control and Instrumentation, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech RepublicReceived 18 March 2011. Revised 30 June 2011. Accepted 30 June 2011. Available online 24 October 2011.View full text Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved – next to other classification methods – by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

prs.rt("abs_end");Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks

Figures and tables from this article:

Fig. 1. Schematic representation. (a) Single neuron with vector input. (b) One-layer network with m neurons.

View Within ArticleFig. 2. Transfer functions. (a) Log-sigmoid. (b) Tan-sigmoid. (c) Hard limit. (d) Linear.

View Within ArticleFig. 3. Extraction of the 4-elements features vector from EEG epoch. PSD – power spectral density, d, ?, a, ß – delta, theta, alpha and beta bands, respectively, drel, ?rel, arel, ßrel – relative power values for delta, theta, alpha and beta bands, respectively.

View Within ArticleTable 1. Summary of artificial neural network (ANN) based systems for sleep scoring. BP: backpropagation, EEG: electroencephalogram, EMG: electromyogram, EOG: electrooculogram (LEOG, REOG: left, right EOG, respectively), FC: fully connected, FT: Fourier transform, MLNN: multilayer neural network, MLP: multilayer perceptron, MT: movement time, RatP: ratio power, REM, rapid eye movement, RMS: root mean square, RP: relative power, RUM, LM: Rumelhart (gradient descent without momentum) and Levenberge-Marquardt learning algorithm, respectively, S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, SD: standard deviation, SOM: self-organizing map, SWS: slow wave sleep, TP: total power, W: wakefulness, WT: wavelet transform. Description of sleep stages is according to R&K and AASM.

View table in articleView Within ArticleTable 2. Output neurons of proposed artificial neural network models. REM: rapid eye movement, S*: stage involving four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View table in articleView Within ArticleTable 3. Results of sleep scoring obtained by proposed artificial neural network (ANN) models. EEG: electroencephalogram, REM: rapid eye movement, RP, relative power, S: stage involving the four stages of non-REM and REM sleep, S*: stage involving the four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View table in articleView Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

prs.rt('data_end');

View the original article here

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