In the field of medical diagnostics, innovation knows no boundaries. Recently, at the intersection of a computer vision and machine learning, an exciting breakthrough appeared, promising to revolutionize the way of assessing and assessing patients, especially those with physical disorders, such as cerebral palsy. This breakthrough development, known as Pose techniqueIt aims to transform the landscape of medical diagnosis and patient care.
Traditionally, the assessment of the motor function of patients, especially in conditions such as cerebral palsy, requires frequent personal visits to the doctor's office. This process can be not only burdensome, but also financially burdensome and emotionally subject to, especially for children and their parents. However, thanks to this most modern technique developed by MIT engineers, we are now on the border of the transformational leap forward in medical assessment.
The heart of this innovation lies a trouble -free integration of computer vision and machine learning. Using these advanced technologies, the position technique has unlocked the remote potential of patients' motor function assessment. The method analyzes patient films in real time, deciphering specific position patterns in these films. This analysis, directed by machine learning algorithms, clinical faces of the motor function.
To develop a technique for analyzing skeletal data in patients with cerebral palsy, a condition usually assessed using a gross motor function classification system (GMFC), scientists use a five -level scale representing the overall motor function of the child (lower numbers indicate higher mobility).
The team used a publicly available set of data posing the skeleton provided by the nervous -muscular Biomechanics Laboratory Stanford University. This set of data contained films with the participation of over 1000 children with cerebral palsy, each of which demonstrates various exercises in the clinical environment. In addition, each video was marked with the result of GMFCS assigned by the clinicist after personal assessment. The Stanford group processed these films using the position estimation algorithm to generate the skeleton posing data, serving as the basis for subsequent myth research.
Interestingly, the position of mapping the position showed an accuracy indicator exceeding 70%, which corresponds to the assessments of clinicians during personal assessment. This level of precision has a huge promise to improve the patient's assessments and reduce the need for frequent and tedious travel to medical facilities.
The potential use of position mapping technique goes far beyond cerebral palsy. The research syndrome is currently adapting the approach to the assessment of children with metachromatic leukodistrophe, a rare genetic disorder affecting the nervous system. In addition, they are actively working on adapting the method of assessing patients who have experienced stroke.
Hermano Krebs, the chief scientist in the MIT Department of Mechanical Engineering, provides for the future in which patients can reduce relying on hospital visits in the field of grades. “We believe that this technology can be potentially used to remotely assess all conditions that affect motor behavior,” he says. This means seismic shift in the way of approaching medical assessments and patient care.
The road to this extraordinary achievement began with a computer vision and algorithms designed to estimate human movements. POSE estimation algorithms paved the way to translating the video sequences into poses. These posts, represented as lines and dots, were then mapped to coordinates for further analysis.
The research syndrome used the spatial and temple nervous network of the chart to decipher the cerebral palsy patterns, classifying the levels of patient mobility. Amazing, network training on a wider set of data, which included movies about healthy adults performing daily activities, significantly increased its accuracy in the classification of patients with cerebral palsy.
The real miracle of this innovation is its availability. The method can be easily done on many mobile devices, ensuring wide availability and processing of films in real time. The MIT team actively develops an application that can enable patients to take control of their self -esteem. This application would allow parents and patients to record movies in the comfort of their homes. The results may then be without effort made available to health care employees, paving the way to more aware and timely interventions. In addition, the possibility of adapting the method includes assessing other neurological disorders, promising to reduce health care costs and increase patient care.
As mentioned, the integration of computer vision and machine learning quickly transforms the landscape of medical diagnostics. In QDATA, we share this enthusiasm for innovative solutions in medical imaging, contributing to a better future of healthcare. Our case studies provide a closer look at our research and ML solutions. Read more about our latest computer detection project of breast cancer-AI solution-based connection to increase the precision and performance of breast cancer diagnosis.
