Medical Image Processing primarily focuses on analyzing medical images to identify and treat diseases. It uses 3D image data sets of the human body obtained through MRI and CT scans to diagnose diseases and pathologies in patients to schedule surgeries or medical interventions accordingly.
However, there are some technical complications that may arise. One such challenge is that in order to identify complex diseases, advanced technologies need to be implemented.
Another challenge that Medical Image Processing faces is that of managing large-scale data. The process of medical imaging generates data on a large scale, which needs to be effectively managed so that it can be analyzed.
Python’s flexibility and scalability make it an excellent tool for Medical Image Processing. Its principal applications in Medical Image Processing are powered by Machine Learning (ML) and Natural Language Processing (NLP) algorithms.
It is a self-determining and scalable open-source software that uses Deep Learning to conduct medical image analysis. It has large pre-defined libraries and functions, so it can support multiple kinds of image processing and solve programming issues.
Additionally, it can perform sophisticated functions such as data handling of full and patch-wise 2D and 3D images and report generation based on independent outcome assessments.
How Is Python Implemented in the Healthcare Field?
Python is an advanced, versatile programming language that develops algorithms to process and analyze images. It has numerous applications in the healthcare industry. Let’s explore a few scenarios below:
1. Detecting Parkinson’s Disease
One of the most important applications of Python in healthcare is detecting Parkinson’s disease. One way of doing so is to use machine learning algorithms that can distinguish between MRI scans of patients with Parkinson’s and MRI scans of patients without Parkinson’s disease.
2. It Is Used to Analyze Data
The healthcare industry generates and collects large amounts of patient data daily. This data needs to be sorted and analyzed by healthcare institutions to provide better patient care and develop newer treatments.
It is also used for building healthcare models based on ML. It uses AI to inspect and break down the patient data that has been collected to forecast predictions.
Moreover, Python offers a lot of machine-learning libraries such as SciPy, NumPy, Pandas, scikit-learn, and many more, using which it can develop machine-learning models.
3. It Is Used for Making Predictions
Another area in the healthcare industry that can benefit from the application of Python is making predictions. AI models developed using Python can be used to predict clinical decision-making. These AI models can predict the patient’s likelihood of developing certain diseases or conditions.
Further, they can also determine the probability of a patient responding to a particular treatment and forecast hospital admissions.
4. Automated Medical Imaging in CT Scans
CT scans thus far, have been conducted using 2D slices from the patient’s brain, abdomen, or chest. However, through the use of Deep Learning, researchers have developed solutions that can analyze 3D image series from a CT scan.
These Deep Learning powered algorithms can be used to develop an integrated application that can analyze chest, C-Spine, abdomen, and head scans without depending on an application for image analysis.
5. Boosting the speed of MRI
MRI is a non-disruptive way of examining the internal working of the body. It is comparatively less risky than a CT scan but takes longer examination time. For example, performing a cardiac MRI might take more than an hour.
With image-based machine learning, the time taken to conduct a cardiac MRI examination can be reduced.
6. Enhanced PET Scan Accuracy
PET Scan is a drug tracer used to detect normal or abnormal metabolic activity in the human body. These scans are helpful in evaluating patient response to treatment, especially in cancer cases. Additionally, early detection and response play a major role when the patient undergoes chemotherapy or radiation therapy.
When healthcare professionals can assess patient response in the initial weeks of treatment, they can adjust and change the dosage to generate a positive response.
7. Improved Ultra Sound Imaging
Machine learning-powered medical imaging is offering promising advancements in the field of medical science. Python can be used to develop algorithms allowing patients to self-scan and capture ultrasounds to aid them in treatment or condition monitoring.
Until now, ultrasound technology involved probes, but this Python-powered technology could revolutionize the ultrasound process for patients, clinicians, and radiologists.
8. Automated X-Rays
Algorithms powered by Python can be used to detect symptoms of Tuberculosis through X-rays. The method uses machine learning to identify abnormalities accurately.
This may be helpful for radiologists, who can use this technology to help patients with early diagnosis and treatment, thus decreasing mortality rates.
9. Improved Image Analysis Software
Image analysis software powered by AI will help radiologists and clinicians. Radiologists will spend less time screening images which in turn, will allow them to focus on patient diagnosis and decision-making.
Furthermore, this technology will also help non-radiologists to interpret these medical images. The intelligence provided by AI-powered image analysis will help paramedics and doctors to interpret images from ultrasound scanners.
10. Timely and Accurate Diagnosis
Advanced image analysis techniques will be helpful for patients as they will be able to avail timely and accurate health diagnosis.
Patients will be able to monitor themselves by using wearable self-scanning devices. Furthermore, patients will have to undergo less severe procedures. There will also be a reduction in the number of scans a patient undergoes.
11. Monitor Treatment Progress
After the patient has been administered treatment, medical imaging may help the physician monitor the progress and efficiency of the treatment.
It can also be helpful to generate a before-and-after comparison. For example, a CT scan can be used to compare the tumor size before and after treatment to check the effectiveness of the treatment.
Python Can Be the Future of Healthcare Imaging
Python has the potential to transform the landscape of medical image processing. It can be used to develop algorithms that can process and analyze images.
It can also help detect anomalies in images that can help healthcare providers provide quicker and more accurate patient diagnoses.
