clinical adoption
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Author(s):  
Matthew J. Gerber ◽  
J. P. Hubschman

Abstract Purpose of Review In this review, we provide a brief history of intraocular robotic surgical systems and review the latest technological advancements. The goals are to (a) provide readers with a clear understanding of the important work that has been done in this field; (b) illuminate existing challenges towards full clinical adoption; and (c) speculate on future directions. Recent Findings The majority of work on intraocular robotic surgical systems has been done in university research settings, although two systems have been evaluated in human clinical trials and one system is commercially available for use in human patients. Summary The future of robotic systems in intraocular surgical procedures will depend on the results of ongoing clinical trials and the success of recent start-up companies. Many challenges remain before such systems can become safe and effective treatment options. However, the future of intraocular robotic surgical systems is bright and full of promise.


2021 ◽  
Vol 2 ◽  
Author(s):  
Rima El-Sayed ◽  
Camille Fauchon ◽  
Junseok A. Kim ◽  
Shahrzad Firouzian ◽  
Natalie R. Osborne ◽  
...  

Conditioned pain modulation (CPM) is a physiological measure thought to reflect an individual's endogenous pain modulation system. CPM varies across individuals and provides insight into chronic pain pathophysiology. There is growing evidence that CPM may help predict individual pain treatment outcome. However, paradigm variabilities and practical issues have impeded widespread clinical adoption of CPM assessment. This study aimed to compare two CPM paradigms in people with chronic pain and healthy individuals. A total of 30 individuals (12 chronic pain, 18 healthy) underwent two CPM paradigms. The heat CPM paradigm acquired pain intensity ratings evoked by a test stimulus (TS) applied before and during the conditioning stimulus (CS). The pressure CPM paradigm acquired continuous pain intensity ratings of a gradually increasing TS, before and during CS. Pain intensity was rated from 0 (no pain) to 100 (worst pain imaginable); Pain50 is the stimulus level for a response rated 50. Heat and pressure CPM were calculated as a change in TS pain intensity ratings at Pain50, where negative CPM scores indicate pain inhibition. We also determined CPM in the pressure paradigm as change in pressure pain detection threshold (PDT). We found that in healthy individuals the CPM effect was significantly more inhibitory using the pressure paradigm than the heat paradigm. The pressure CPM effect was also significantly more inhibitory when based on changes at Pain50 than at PDT. However, in individuals with chronic pain there was no significant difference in pressure CPM compared to heat or PDT CPM. There was no significant correlation between clinical pain measures (painDETECT and Brief Pain Inventory) and paradigm type (heat vs. pressure), although heat-based CPM and painDETECT scores showed a trend. Importantly, the pressure paradigm could be administered in less time than the heat paradigm. Thus, our study indicates that in healthy individuals, interpretation of CPM findings should consider potential modality-dependent effects. However, in individuals with chronic pain, either heat or pressure paradigms can similarly be used to assess CPM. Given the practical advantages of the pressure paradigm (e.g., short test time, ease of use), we propose this approach to be well-suited for clinical adoption.


Author(s):  
Michael John Parkes ◽  
Stuart Green ◽  
Jason Cashmore ◽  
Qamar Ghafoor ◽  
Thomas Clutton-Brock

Objective: Single prolonged breath-holds of >5 min can be obtained in cancer patients. Currently, however, the preparation time in each radiotherapy session is a practical limitation for clinical adoption of this new technique. Here, we show by how much our original preparation time can be shortened without unduly compromising breath-hold duration. Methods: 44 healthy subjects performed single prolonged breath-holds from 60% O2 and mechanically induced hypocapnia. We tested the effect on breath-hold duration of shortening preparation time (the durations of acclimatization, hyperventilation and hypocapnia) by changing these durations and or ventilator settings. Results: Mean original breath-hold duration was 6.5 ± 0.2 (standard error) min. The total original preparation time (from connecting the facemask to the start of the breath-hold) was 26 ± 1 min. After shortening the hypocapnia duration from 16 to 5 min, mean breath-hold duration was still 6.1 ± 0.2 min (ns vs the original). After abolishing the acclimatization and shortening the hypocapnia to 1 min (a total preparation time now of 9 ± 1 min), a mean breath-hold duration of >5 min was still possible (now significantly shortened to 5.2 ± 0.6 min, p < 0.001). After shorter and more vigorous hyperventilation (lasting 2.7 ± 0.3 min) and shorter hypocapnia (lasting 43 ± 4 s), a mean breath-hold duration of >5 min (5.3 ± 0.2 min, p < 0.05) was still possible. Here, the final total preparation time was 3.5 ± 0.3 min. Conclusions: These improvements may facilitate adoption of the single prolonged breath-hold for a range of thoracic and abdominal radiotherapies especially involving hypofractionation. Advances in knowledge: Multiple short breath-holds improve radiotherapy for thoracic and abdominal cancers. Further improvement may occur by adopting the single prolonged breath-hold of >5 min. One limitation to clinical adoption is its long preparation time. We show here how to reduce the mean preparation time from 26 to 3.5 min without compromising breath-hold duration


2021 ◽  
Author(s):  
Coralie Joucla ◽  
Damien Gabriel ◽  
Emmanuel Haffen ◽  
Juan-Pablo Ortega

Research in machine-learning classification of electroencephalography (EEG) data offers important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but the clinical adoption of such systems remains low. We propose here that much of the difficulties translating EEG-machine learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model development (normalization, hyperparameter optimization and cross-validation) and show that, while these 3 aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.


2021 ◽  
Author(s):  
Anna G. Green ◽  
Chang H. Yoon ◽  
Michael L. Chen ◽  
Luca Freschi ◽  
Matthias I. Gröschel ◽  
...  

AbstractLong diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited in part by a lack of interpretability and verifiability, especially in deep learning methods. Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates. The CNN performs with state-of-the-art levels of predictive accuracy. Evaluation of salient sequence features permits biologically meaningful interpretation and validation of the CNN’s predictions, with promising repercussions for functional variant discovery, clinical applicability, and translation to phenotype prediction in other organisms.


2021 ◽  
Vol 38 (05) ◽  
pp. 565-575
Author(s):  
Anna S. Christou ◽  
Amel Amalou ◽  
HooWon Lee ◽  
Jocelyne Rivera ◽  
Rui Li ◽  
...  

AbstractImage-guided robotics for biopsy and ablation aims to minimize procedure times, reduce needle manipulations, radiation, and complications, and enable treatment of larger and more complex tumors, while facilitating standardization for more uniform and improved outcomes. Robotic navigation of needles enables standardized and uniform procedures which enhance reproducibility via real-time precision feedback, while avoiding radiation exposure to the operator. Robots can be integrated with computed tomography (CT), cone beam CT, magnetic resonance imaging, and ultrasound and through various techniques, including stereotaxy, table-mounted, floor-mounted, and patient-mounted robots. The history, challenges, solutions, and questions facing the field of interventional radiology (IR) and interventional oncology are reviewed, to enable responsible clinical adoption and value definition via ergonomics, workflows, business models, and outcome data. IR-integrated robotics is ready for broader adoption. The robots are coming!


2021 ◽  
Author(s):  
Whitney Rhodes ◽  
Richard W DeClue ◽  
Neil A Accortt ◽  
Ran Jin ◽  
Darcie Sandschafer ◽  
...  

Aim: Evaluated real world use of bevacizumab-awwb (MVASI®), a bevacizumab biosimilar, for treating metastatic colorectal cancer (mCRC). Materials & methods: Adult mCRC patients who received bevacizumab-awwb during the first year after market availability were identified from the ConcertAI oncology dataset. Results: Of 304 patients, 47% initiated bevacizumab-awwb as reference product (RP) naive patients and 53% received bevacizumab-awwb with prior exposure to RP. Overall, 78% received bevacizumab-awwb as first-line therapy; the proportion was higher (91%) in RP-naive patients. Among RP-experienced patients, 83% were transitioned from RP to bevacizumab-awwb in the same line without disease progression; of those, the majority (83%) were transitioned within 28 days. Conclusion: Early evidence from US oncology practices suggests clinical adoption of bevacizumab-awwb in treating mCRC patients.


2021 ◽  
Vol 9 ◽  
Author(s):  
Rashaad Bhyat ◽  
Simon Hagens ◽  
Katie Bryski ◽  
Jocelyn Fausto Kohlmaier

Digital health has massive potential in health care but has been slow to evolve in comparison to other information-intensive industries, which have more readily taken advantage of new technology. One of the key barriers has been the complex relationship between the perceived return on investment for the investor and the resulting value to patients and caregivers. Those actors who pay for technologies do not always see an appreciable return for themselves, while those actors who must apply the technology to generate value are not always incentivized to do so. This misalignment across health system payers and administrators, clinicians and patients must be better understood and addressed to help accelerate digital health. This paper will examine this challenge through the clinician experience, using empirical case examples from Canada to illustrate opportunities for change. While many factors may influence digital health adoption, this paper specifically aims to explore the shifts in the balance of the perceived value of implementing digital health tools, vs. the efforts required to adopt them. It will explore two contrasting case examples: clinical adoption of EMRs in Canada from 2009 to 2015, and clinical adoption of virtual care technologies during the COVID-19 pandemic from 2020 to 2021. In 2006, Canada lagged peer countries significantly in the adoption of electronic medical records (EMR) in community-based care. Financial support and cooperation of multiple levels of government and clinical stakeholders were required to address the misaligned incentives, which led to significant uptake by care providers. The rapid adoption of virtual care in Canada in response to the pandemic provides another relevant example of the importance of alignment among the factors of clinical workflows, clinical appropriateness, technology integration and payment models. Experts have highlighted the need for standardization, regulation, and clear policy to ensure sustainable, high quality virtual care that complements in-person care. In both cases, the costs and effort of adopting new technologies outweighed direct clinician value, requiring change initiatives to catalyze progress. This imbalance could be unique to these examples in Canada, and may not be globally generalizable to the adoption of all digital health tools. However, how change efforts can be tailored to adjust to a rapidly evolving health care workforce, spanning diverse jurisdictions and stakeholder groups will be critical to the sustainability of virtual care adoption. Furthermore, what key elements must be considered to guide change initiatives for successful implementation, designed to influence change while adding value for patients, clinicians and Canada's health care systems? Using insights from successful change initiatives past and present, this paper aims to answer these questions to enable a smoother transition to digital health innovations of the future.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Catherine R. Virelli ◽  
Ayeshah G. Mohiuddin ◽  
James L. Kennedy

AbstractPharmacogenomics (PGx) is the study of genetic influences on an individual’s response to medications. Improvements in the quality and quantity of PGx research over the past two decades have enabled the establishment of commercial markets for PGx tests. Nevertheless, PGx testing has yet to be adopted as a routine practice in clinical care. Accordingly, policy regulating the commercialization and reimbursement of PGx testing is in its infancy. Several papers have been published on the topic of challenges, or ‘barriers’ to clinical adoption of this healthcare innovation. However, many do not include recent evidence from randomized controlled trials, economic utility studies, and qualitative assessments of stakeholder opinions. The present paper revisits the most cited barriers to adoption of PGx testing: evidence for clinical utility, evidence for economic effectiveness, and stakeholder awareness. We consider these barriers in the context of reviewing PGx literature published over the past two decades and emphasize data from commercial PGx testing companies, since they have published the largest datasets. We conclude with a discussion of existing limitations to PGx testing and recommendations for progress.


Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4767
Author(s):  
Kevin O’Dwyer ◽  
Katarina Domijan ◽  
Adam Dignam ◽  
Marion Butler ◽  
Bryan M. Hennelly

Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.


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