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Exactech and KenSci Publish Research on the Impact of Artificial Intelligence to Predict Clinical Outcomes after Shoulder Arthroplasty

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GAINESVILLE, Fla.--(BUSINESS WIRE)--Exactech, a developer and producer of innovative implants, instrumentation and computer-assisted technologies for joint replacement surgery, and KenSci, a healthcare artificial intelligence (AI) platform company, announced today that a collaborative, foundational study on using machine learning (ML) to predict outcomes after shoulder arthroplasty has been published in Clinical Orthopaedics and Related Research, one of the premier scientific journals in orthopaedics. The research analyzes the potential of ML to use preoperative data to anticipate patients' post-operative results after anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA). These results can help surgeons preoperatively identify if a patient will achieve certain clinical improvement thresholds to appropriately risk-stratify patients for these elective procedures. Specifically, this research explores the efficacy of ML to predict the American Shoulder and Elbow Surgery (ASES), Constant, global shoulder function and VAS pain score, as well as to predict a patient's active range of motion in abduction, forward flexion and external rotation. This research also studies the ability of ML to identify if a patient may achieve clinical improvement that exceeds the minimal clinically important difference threshold as well as the substantial clinical benefit threshold for each outcome measure.


Worth the cost? A closer look at the da Vinci robot's impact on prostate cancer surgery

Nature

Urology fellow, Jeremy Fallot, and nurse, Shauna Harnedy, assist in robotic surgery by Ruban Thanigasalam (out of view) in Sydney, Australia.Credit: Ken Leanfore for Nature Loved by surgeons and patients alike for its ease of use and faster recovery times, the da Vinci surgical robot is less invasive than conventional procedures, and lacks the awkwardness of laparoscopic (keyhole) surgery. But the robot's US$2-million price tag and negligible effect on cancer outcomes is sparking concern that it's crowding out more affordable treatments. There are more than 5,500 da Vinci robots globally, manufactured by California-based tech giant, Intuitive. The system is used in a range of surgical procedures, but its biggest impact has been in urology, where it has a market monopoly on robot-assisted radical prostatectomies (RARP), the removal of the prostate and surrounding tissues to treat localized cancer. Uptake in the United States, Europe, Australia, China and Japan for performing this procedure has been rapid.


Artificial intelligence in spine care is "here to stay"

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Artificial intelligence (AI) has "tremendous potential" to revolutionise comprehensive spine care across areas including patient selection, outcome prediction, research, pre-operative workup and peri-operative assistance, the authors of a large systematic review on the topic have found. Published in the Global Spine Journal, the review, led by Jonathan J Rasouli (Cleveland Clinic, Cleveland, USA) looks at the current trends and applications of AI and machine learning in conventional and robotic-assisted spine surgery. According to Rasouli and colleagues, there has been increasing attention and interest in the system-based benefits of AI and its applications to spine surgery. This includes helping clinicians and hospital centres define the quality and cost of care, improve outcomes and mitigate downrange financial exposures to both institutions and payers. "While there has also been controversy surrounding AI, if implemented appropriately, it has the potential to revolutionise the standard of care in spine surgery, reduce cost and waste, and improve the efficiency and patient care. In addition, AI could enhance individualised care to patients to reduce heterogeneity in both clinical practice and research," the study team writes.


Artificial intelligence in spine care is "here to stay"

#artificialintelligence

Artificial intelligence (AI) has "tremendous potential" to revolutionise comprehensive spine care across areas including patient selection, outcome prediction, research, pre-operative workup and peri-operative assistance, the authors of a large systematic review on the topic have found. Published in the Global Spine Journal, the review, led by Jonathan J Rasouli (Cleveland Clinic, Cleveland, USA) looks at the current trends and applications of AI and machine learning in conventional and robotic-assisted spine surgery. According to Rasouli and colleagues, there has been increasing attention and interest in the system-based benefits of AI and its applications to spine surgery. This includes helping clinicians and hospital centres define the quality and cost of care, improve outcomes and mitigate downrange financial exposures to both institutions and payers. "While there has also been controversy surrounding AI, if implemented appropriately, it has the potential to revolutionise the standard of care in spine surgery, reduce cost and waste, and improve the efficiency and patient care. In addition, AI could enhance individualised care to patients to reduce heterogeneity in both clinical practice and research," the study team writes.


Origami Surgical raises $2.2m for robotic suturing tech - MassDevice IAM Network

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Origami Surgical last week filed an SEC Form D to confirm the sale of more than $2.2 million in an equity offering.The Madison, N.J.-based company, founded this year, filed a new notice for the sale of equity on March 23, 2020, with the intention of the offering lasting less than a year. According to the Form D filing, Origami Surgical's offering is not being made in connection with a business combination transaction, such as a merger, acquisition or exchange offer. Two investors contributed to the sale of $2,224,998 in the equity offering that is set to bring in $2,499,996, leaving $274, 998 left to be sold. Origami Surgical did not list an intended use of proceeds. The company develops the StitchKit, which it touts as the first suture system designed to improve endoscopic robotic surgery outcomes by increasing efficiency, autonomy and safety, according to the company website.


Key Challenges That Healthcare AI Needs to Overcome in 2020 - Dataconomy

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The promise of artificial intelligence (AI) is finally being realized across a wide variety of industries. AI is now viewed as a crucial technology to adopt for enterprises to thrive in today's business environment. Healthcare, in particular, has been one of the industries that AI advocates expect to be revolutionized by AI. Potential use cases paint a clear picture of how healthcare stakeholders stand to benefit from AI in the months ahead. Patient care standards are projected to improve, diagnostic capabilities are expected to expand, and facilities should become far more efficient.


The Heat-Up Game of Robotic Surgery Companies Analytics Insight

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The multi-limbed da Vinci can be utilized in a variety of procedures, including cardiovascular, colorectal, gynaecological, head and neck, thoracic and urologic medical procedures, however, only if they're minimally invasive. How large the market could be is as yet hazy, yet experts concur the potential still can't seem to be tapped. So more players are moving in, and rapidly. As the beginning of robotic surgery offers an approach to increasingly precise control and better patient results, early pioneers like Intuitive Surgical Inc. are seeing increased pressure from large organizations like Johnson and Johnson and Medtronic PLC, which have made major M&A investments to break into the market as of late. Intuitive's da Vinci system was first affirmed by the U.S. Food and Drug Administration in 2000 for urology.


DAISI: Database for AI Surgical Instruction

arXiv.org Artificial Intelligence

Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. Nonetheless, expert mentors are often unavailable to provide trainees with real-time medical guidance. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. However, AI/autonomous mentoring in medicine has been limited by the availability of generalizable prediction models, and surgical procedures datasets to train those models with. This work presents the initial steps towards the development of an intelligent artificial system for autonomous medical mentoring. Specifically, we present the first Database for AI Surgical Instruction (DAISI). DAISI leverages on images and instructions to provide step-by-step demonstrations of how to perform procedures from various medical disciplines. The dataset was acquired from real surgical procedures and data from academic textbooks. We used DAISI to train an encoder-decoder neural network capable of predicting medical instructions given a current view of the surgery. Afterwards, the instructions predicted by the network were evaluated using cumulative BLEU scores and input from expert physicians. According to the BLEU scores, the predicted and ground truth instructions were as high as 67% similar. Additionally, expert physicians subjectively assessed the algorithm using Likert scale, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.


New technologies to transform healthcare in 2020

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Can you trust a robot with your life? Apparently you can! Robots such as the da Vinci surgical robot can help medical professionals in the operating room and the scope of this extends beyond surgical applications. With the global medical robotics market expected to reach $20 billion by 2023, robots in healthcare are set to perform many different tasks. They are already helping doctors treat patients in rural areas with telepresence, providing medical supplies, sterilising hospital rooms, helping patients with rehabilitation or with prosthetics, and automating labs and packaging medical devices. Besides, there's the micro-bot that can deliver therapy to a specific part of the body, such as radiation to a tumour or cure bacterial infections.


Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach

arXiv.org Artificial Intelligence

Emerging applications in robotics and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when information about system models is limited. In this regard, we propose a model-free safety specification method that learns the maximal probability of safe operation by carefully combining probabilistic reachability analysis and safe reinforcement learning (RL). Our approach constructs a Lyapunov function with respect to a safe policy to restrain each policy improvement stage. As a result, it yields a sequence of safe policies that determine the range of safe operation, called the safe set, which monotonically expands and gradually converges. We also develop an efficient safe exploration scheme that accelerates the process of identifying the safety of unexamined states. Exploiting the Lyapunov shielding, our method regulates the exploratory policy to avoid dangerous states with high confidence. To handle high-dimensional systems, we further extend our approach to deep RL by introducing a Lagrangian relaxation technique to establish a tractable actor-critic algorithm. The empirical performance of our method is demonstrated through continuous control benchmark problems, such as a reaching task on a planar robot arm.