AI News, Advances in Orthopedic Diagnosis Using Artificial Intelligence ... artificial intelligence

Artificial Intelligence in Joint Arthroplasty

Artificial intelligence (AI) has transitioned from theory to reality in teaching machines to automate tasks to better the lives of our patients, and transformed a variety of diseases through supportive physician decision-making.

This issue of Arthroplasty is meant to help bring forth advances of AI in arthroplasty, as we embrace the wave of AI that will certainly orient arthroplasty surgeons concerning healthcare in the future.Overall, this Special Issue is established as an open platform for exchanging ideas of addressing advances of AI for joint arthroplasty.

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

between 1991 and 2000, 2001 and 2010, and 2011 and 2020, there was a progressive increase in publications from 7 to 12 and 43 respectively, clearly indicating an exponential rise in this subject due to technological advancements and the continuous digitization of orthodontics.

The results reveal that artificial neural networks (ANNs) were the widely utilized AI/ML algorithm (10) followed by convolutional neural networks (CNNs), support vector machine (SVM)-8 studies and regression (logistic and linear) in 8 studies apart from 23 other algorithms utilized in various studies.

Each domain was addressed with the PICO framework for literature evaluation and can be enumerated as (1) diagnosis and treatment planning—either broad based or specific, (2) automated anatomic landmark detection and/or analyses, (3) assessment of growth and development, (4) evaluation of treatment outcome and finally a (5) miscellaneous category.

Several studies since then [27, 32, 35, 37, 64, 65, 69, 70, 74, 77, 78] have affirmed greater accuracy of landmark detection, reduced time, and human effort spent on anatomic landmark detection and/or analyses with AI/ML as compared to traditional methods.

At least one study [19] included in this review compared frontal cephalometric landmarking ability of humans versus that of artificial neural networks and the results showed that ANNs could achieve accuracy comparable to humans in placing cephalometric points, and in some cases surpasses the accuracy of inexperienced doctors (students, residents, graduate students).

However, thus far, no tools exist to lead patients and clinicians out of the decision-making uncertainty in which they are trapped, especially when they face a condition that has several possible correct treatment options and orthodontists over the years have attempted to create systems that take the subjective bias out of diagnostic decision-making.

Unsurprisingly, the most significant number of studies [18, 23, 25, 28, 68] under the diagnosis domain was found to investigate the development of several decision support systems that reduce the relative subjectivity or increased complexity of making the extraction decision.

[21] expanded the use of ANNs to determine the diagnosis of orthognathic surgery in addition to the extraction decision and their study results showed a 96% success rate for diagnosis of surgery/non-surgery decision and 91% success rate for detailed diagnosis of surgery type and extraction decision.

The same author [73] also attempted to identify critical peculiarities of class II and class III malocclusions and demonstrated that class II subjects exhibited few highly connected orthodontic features, while class III patients showed more compact network structure characterized by strong co-occurrence of normal and abnormal clinical functional and radiological features.

group of researchers have specifically studied the applications of AI and ML for the detection of TMJ osteoarthritis [22, 47, 66, 67] and have concluded that deep learning neural network was the most accurate method for classification of TMJ-OA that allows disease staging of bony changes in TMJ-OA.

[71] developed a machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for segmentation (LINKS) used with CBCT images to quantify volumetric skeletal maxilla discrepancies and suggested palatal expansion could be beneficial for those with unilateral canine impaction, as underdevelopment of maxilla often accompanies canine impaction in early teen years.

[39] proposed and tested several deep learning approaches to assess skeletal bone age automatically in what was one of the first studies for an automated skeletal bone age assessment, tested on a public dataset and for all age ranges, races, and genders and with a source code available.

A recent study [20] compared seven artificial intelligence algorithms—k-nearest neighbors, Naïve Bayes, decision tree, artificial neural networks, support vector machines, random forest, and logistic regression algorithms to determine the preferred method of cervical vertebrae maturation and concluded that ANN was the most stable and preferred method of determining the same.

Applications of AI and ML that could not be described under the above four major domains were grouped under the miscellaneous category in this review and these include automated tooth segmentation either from CBCT images or dental models [33, 43], detection of activation pattern of tongue musculature [50] and evaluation of effects of a different curing unit and light-tips on temperature increase during orthodontic bonding [26].

Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries

The delayed adoption of this technology is likely multifactorial, with high development costs, complexity of use in absence of exposure or experience, and ethical considerations all playing a role.9 Moreover, there is evidence suggesting that both health care providers and patients may distrust AI use in the health care setting.18,23 The focus of this systematic review was to provide readers with a summary of these investigations that have examined the application of AI in the management of ACL injury.

Furthermore, it was demonstrated that despite a high sensitivity and specificity for assessing chronic ACL injury, the sensitivity and specificity of the anterior drawer test decreased to 49% and 58%, respectively, when assessing acute tears.4 In addition to these findings, there was only moderate interobserver reliability for the Lachman, anterior drawer, and pivot-shift tests when performed by professionals who are not orthopaedic surgeons.36 This is important, as many of these individuals are the first to assess these patients and they are frequently responsible for initiating further work-up and expert consultation.

Although there is little published literature on the integration of ML in clinical diagnosis, AI has been more extensively researched to aid in the interpretation of radiologic images, such as MRI.13 Despite being heavily relied on for diagnosis and preoperative planning, MRI findings regarding ACL injury can be subject to a high degree of inaccuracy.32 Multiple investigations presented in this review demonstrated the ability to train algorithms capable of not only assisting human diagnosis but also independently diagnosing ACL injury and other knee pathology with a high degree of accuracy.5,22,40,48 Deep learning also shows promise in MRI protocol optimization.38 Protocol development usually requires radiologists to read multiple images at timed intervals to limit bias.

The true power of this technology likely lies in its ability to form predictions from large preexisting data sets, lending itself well to prognostication using biomechanical data.9 There is a significant body of literature examining the role of biomechanical factors and kinematic parameters in pathogenesis of ACL tears, some of which have been used to identify at-risk individuals.15 However, because of the ability of ML algorithms to pick up complex, nonlinear relationships, this technology has the potential to extract even more important anatomic and biomechanical features that may predispose athletes to noncontact ACL injury.

Ultimately, the parameters could be used to develop risk assessment tools to identify athletes who may benefit from validated ACL injury prevention programs focusing on quadriceps, hamstring, and core activation exercises, such as FIFA11+.1 Furthermore, this technology could be used to counsel young athletes on their individual risk of ACL injury when participating in a particular sport based on their knee characteristic and could also help teams selecting athletes based on the risk analysis.

These findings may have important implications, as FNBs have been demonstrated to not only reduce postoperative pain but also to reduce opioid intake.10 Future studies should focus on patient selection for saphenous nerve blocks, which are gaining in popularity because of their theoretical advantage in avoiding quadriceps motor blockade but may still be associated with complications, including unexpected muscle weakness.6 The studies highlighted in the present review also demonstrate the potential role of ML in the rehabilitation phase after ACL reconstruction.

Third, ML is known to exhibit a “Black Box” phenomenon, whereby little or no information is given regarding the output generated.8,9 This limitation could lead to false discoveries, where the classifier is developed using a data set that has an incorrect association, making it of little use when applied to real cases.9 Conversely, algorithms could also be trained to contain certain biases that may serve to favor decisions that benefit private interests instead of patients.8 The development of algorithms capable of providing some justification for the output they generate would be of particular interest to ML applications in medicine and surgery.3 Ultimately, to address these and other limitations presented by the adoption of ML in orthopaedic surgery, surgeons will need to work in close collaboration with data scientists to fully understand the proper way to evaluate the validity of the output provided by the algorithms and to ensure it is done in an ethical manner.

Contributed: Top 10 Use Cases for AI in Healthcare | MobiHealthNews

AI, machine learning (ML), natural language processing (NLP) and deep learning (DL) enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster with more accuracy, using data patterns to make informed medical or business decisions quickly.

How AI works in healthcare AI is able to analyze large amounts of data stored by healthcare organizations in the form of images, clinical research trials and medical claims, and can identify patterns and insights often undetectable by manual human skill sets.

AI algorithms can analyze these datasets at high speed and compare them to other studies in order to identify patterns and out-of-sight interconnections. The process enables medical imaging professionals to track crucial information quickly.

By using convolutional neural networks, a technology similar to the one that makes cars drive by themselves, AtomNet was able to predict the binding of small molecules to proteins by analyzing hints from millions of experimental measurements and thousands of protein structures.

AI can seek, collect, store and standardize medical data regardless of the format, assisting repetitive tasks and supporting clinicians with fast, accurate, tailored treatment plans and medicine for their patients instead of being buried under the weight of searching, identifying, collecting and transcribing the solutions they need from piles of paper formatted EHRs.

By combining the best elements of biology, data science and chemistry with automation and the latest AI advances, the founding company of this platform is able to generate around 80 terabytes of biological data that is processed by AI tools across 1.5 million experiments weekly.

With an estimated 11% of deaths in hospitals following a failure to identify and treat patients, the early prediction and treatment of these cases can have a huge impact to reduce life-long treatment and the cost of kidney dialysis.  In 2019, the Department of Veterans Affairs (VA) and DeepMind Health created a ML tool that can predict AKI up to 48 hours in advance.

ML can play an essential role in supporting emergency medical staff. In the future medical units could use the technology to respond to emergency calls with automatic defibrillators equipped drones or with CPR-trained volunteers, which would increase the chances for survival in cases of cardiac arrest that take place in the community.

In order to assist clinicians to make informed decisions regarding radiation therapy for cancer patients, Oncora Medical delivered a platform that collects the relevant medical data of patients, evaluates the quality of care provided, optimizes treatments and provides thorough oncology outcomes, data and imaging.

NLP and ML can read the entire medical history of a patient in real time, connect it with symptoms, chronic affections or an illness that affects other members of the family. They can turn the result into a predictive analytics tool that can catch and treat a disease before it becomes life-threatening.

CloudMedX solutions have already been applied in several high-risk diseases such as renal failure, pneumonia, congestive heart failure, hypertension, liver cancer, diabetes, orthopedic surgery and stroke, with the stated objective to lower costs for patients and clinicians by assisting in early and accurate diagnoses of patients.

The company's experts are working on “Project Saturn,” a drug system based on AI molecular biology that assesses more than 69 billion oligonucleotide molecules in silico (conducted or produced by means of computer modeling or computer simulation) against 1 million target sites in order to monitor cell biology to unlock greater potential treatments and therapies.  The discovery and development of genetic medicine brings benefits to patients and clinicians by decreasing the costs associated with the treatment of rare diseases.

AI supports health equity The AI and ML industry has the responsibility to design healthcare systems and tools that ensure fairness and equality are met, both in data science and in clinical studies, in order to deliver the best possible health outcomes.

By improving workflows and operations, assisting medical and nonmedical staff with repetitive tasks, supporting users in finding faster answers to inquiries, and developing innovative treatments and therapies, patients, payers, researchers and clinicians can all benefit from the use of AI in healthcare.

2021 AIMI-HAI Call for Proposals – AI in Medicine Health

In particular, AI will enhance patient outcomes via systems that increase diagnostic accuracy, advance drug development, provide more sophisticated patient monitoring, and personalize care to the needs of the individual.  To achieve the promise of AI in healthcare, significant challenges must be overcome to ensure safe, equitable, and robust systems.

In addition to the above criteria, preference will be given to projects that relate both to the mission of AIMI and the three broad HAI research areas:  AIMI Mission: AIMI develops and supports transformative medical AI applications and the latest in applied computational and biomedical imaging research to advance patient health.

Please submit using the Apply button below.  The proposal (maximum 3 pages, excluding references, PDF, single-spaced, 11 point, 1 inch margins) should include the following components: First two pages: Third page: Fourth page +  Proposals will be reviewed based on: