Artificial intelligence and medical image analysis

by Luis Daniel Fernádez | Dec 5, 2024 | AI in medicine

The progress of new technologies has enabled a great evolution in the field of medicine. Nowadays, artificial intelligence (AI) has become a fundamental tool in different medical specialties, among which the area of image diagnosis. The integration of AI in medical diagnostics offers a multitude of benefits: increased accuracy and quality of diagnostics, early disease detection, task automation, workflow optimization, creation of personalized treatments and preventive measures.

Obtaining a rapid, accurate and effective diagnosis is a key aspect of achieving more efficient healthcare. The use of traditional methods involves the analysis of a large amount of data and the performance of tasks that involve a large amount of data. investment of time and resources. In addition to these aspects, there is also the limitation of the human subjectivityThe use of algorithms for the diagnosis and treatment of the disease, which can lead to errors in clinical practice. In this sense, the use of Artificial Intelligence in medicine has had a remarkable impact on diagnostic imaging. In the following article, we look at how AI that analyzes medical images works and its main applications.

Artificial Intelligence techniques in the analysis of medical images.

Artificial intelligence studies, designs and develops computerized computer systems based on algorithms. that can emulate some of the functions performed by humans, such as thinking and learning to solve problems. An algorithm consists of a set of computer instructions that are designed to perform a specific task. In recent years, a number of tools have emerged, such as the AI-enabled software that use artificial intelligence to automate many tasks and functions in the clinical setting.

What type of technology is used in medical imaging and how does it work? We can differentiate between different techniques:

Machine Learning (ML)

Machine Learning (ML) is a field of artificial intelligence which consists of the use of computer algorithms to analyze and classify data, to learn from it and to make future predictions. The system must comply with a training phase which is referred to as supervised. During this process, medical images are entered with their corresponding, manually implemented labels. As more data is exposed, the algorithm learns to give a specific answer by evaluating different hand-labeled tests.

Most imaging systems make use of this type of artificial intelligence and it is important that, before using it in clinical practice, the system has been tested and validated. One of its main uses is to predict diseases at an early stage. For example, analyzing the probability that a breast lump visible on mammography is a malignant tumor.

Representational Learning or Representation Learning (RL)

Representation Learning (RL) is a sub-type of Machine Learning (ML) that does not require image features to be labeled by hand. Computer algorithm learns on its own the necessary characteristics to classify the data provided. Therefore, human subjectivity is eliminated, i.e., the limitation of analyzing those characteristics that the human being considers relevant. This system is called unsupervised learning and, if sufficient data is provided, the performance that can be obtained is superior to traditional ML.

Deep Learning (DL)

Deep Learning (DL) is an advanced form of Representation Learning (RL). This type of algorithm is in charge of exploring the use of artificial neural networks.based on the structure and function of the human brain. The artificial network of neurons is composed of different layers and connections. Through each layerIn addition, a series of data is propagated that is linked to the performance of a specific task.

In the area of diagnostic imaging, each layer is responsible for analyzing a characteristic of the medical image and assigning a value to it. Subsequently, the final layers of neurons are responsible for collecting all the information and providing a result. This type of technology has great potential and interest in medical image analysis, as it allows multiple uses. From the automatic detection of a lesion in the images and suggest differential diagnoses to structure a report in a preliminary way.

6 applications of AI in medical image analysis

Artificial Intelligence has the ability to process large amounts of data and recognize complex patterns. We can highlight the following applications in the field of diagnostic imaging:

1. Attendance at the radiologist's work

Power of Attorney manage patients' medical records electronically is a very important step forward, since it facilitates the work of the various medical teams involved in the diagnostic imaging process. AI can help to highlight the most relevant data and propose a specific planning of the study to provide information to the different professionals: the clinician, the technician and the radiologist.

2. Optimization of the radiological technique

Using Deep Learning (DL) methods, the algorithms allow for reconstruct images in medical techniques such as magnetic resonance imaging and computed axial tomography, or TAC. With this, the quality of medical images can be increased, making the most of the technical and physical resources available. Another advantage offered by AI is that it makes it possible to establish the ideal amount of radiation for each patient, avoiding the addition of unnecessary radiation.

3. Segmentation and lesion detection

Through the use of AI, the systems can understand the visualized images of an examination and differentiate healthy structures from pathological areas.

4. Classification and diagnosis of pathologies

There are different machine learning algorithms that can identify specific patterns and characteristics in medical imaging for classify them into different disease categories. Currently, algorithms are being developed for the detection of tumors in mammography images and skin cancer in dermoscopy images. In this field, AI can identify cancerous tissues and classify them into specific cancer types, which can lead to faster and more accurate diagnostics.

5. Prediction of treatment response

Artificial Intelligence can also predict patient response to different treatments. Algorithms can access patient data and medical studies with the diagnosis of the patient's disease. With all this information, the patient's response to various treatment options can be predicted. This offers many advantages, as the following can be developed specific treatment plans with a personalized approachadapted to the needs of each patient.

6. Early detection of diseases

Another of the applications of AI in medicine is the early detection of diseases. Through the analysis of large amounts of data, it is possible to detect patterns that may be missed by traditional techniques. For example, one of the uses recently offered by machine learning algorithms is to be able to detect early changes in magnetic resonance images of the brain, which may be indicative of diseases such as Alzheimer's disease.

AI-assisted medical diagnostics is evolving rapidly. Ongoing research is seeking to refine existing AI models with the aim of exploring new applications to provide much more accurate, efficient and faster medical care.

Luis Daniel Fernandez Perez

Director of Diagximag. Distributor of medical imaging equipment and solutions.

AI applications in medicine and their impact on society

by 4D Medical | Nov 12, 2024 | AI in medicine

The use of new technologies and artificial intelligence (AI) has meant a before and after for many sectors. One of them has been medicine, where the latest advances and applications have been influenced by the development of technology. Artificial intelligence is a specialty in the field of computer science that is used to produce programs through a series of algorithms that have the ability to think, learn and make decisions, as humans do.

How does AI work?

AI began to be developed in the 1990s with the aim of creating a computer system that would process data in a similar way to the human brain. One of the branches of artificial intelligence that is most useful in the healthcare sector is the automatic learning. This system has the ability for machines to use the algorithms and learn from the dataThis improves decision making with the processed information.

Through the use of a AI softwareIn addition, a number of functions and tasks can be automated, allowing healthcare professionals to process and analyze medical data more quickly and accurately. This has a significant impact on the different areas of the health care sector and promotes improved healthcare management. Among the main uses offered by AI in the healthcare field, we find that it helps to develop and optimize processes in clinical diagnosis, disease detection and prevention, healthcare, research and the creation or updating of new drugs.

In turn, it has also been a determining factor in the progress of telemedicine and in the development of personalized medical treatments. In the following article, we address the key applications of the AI in medicine and how they are helping to create a more complete, agile and effective healthcare system.

AI applications in medicine

In recent years, AI has been incorporated into medicine to promote higher quality patient care, speed up processes and achieve increased diagnostic accuracy. What are the different areas in which artificial intelligence is currently being used and what improvements have they brought about?

Disease prevention and early diagnosis

AI is a key tool in disease prevention. Through the use of Big Datawhich consists of a combination of digital health data, genomic data and patient behavioral data, can be used as a basis for the development of a new identify risk factors and patterns that lead to the development of certain diseases.

• Spread of diseasesOn the one hand, machine learning algorithms can predict the spread of diseases such as influenza or COVID-19, anticipating epidemic peaks and allowing preventive measures to be taken.
• Detecting signs of chronic diseasesAnother of its applications is that early signs of chronic diseases, such as diabetes or heart disease, can be identified. Chronic diseases are characterized by their slow onset and, in most cases, go unnoticed until they develop into more serious complications. Therefore, the use of AI is very useful for detecting possible signs of disease in medical studies, such as blood tests, ultrasound images or electrocardiograms. In this case, AI algorithms can detect patterns of cardiovascular disease through medical images such as the magnetic resonance imaging or the computed tomography scans.
• Predisposition to genetic diseasesThrough the use of genomic data, artificial intelligence can also analyze predisposition to genetic diseases. AI algorithms are responsible for studying patterns in DNA to identify genetic variants that could indicate a high risk in the development of certain diseases. In oncology, it is used to predict the risk of breast or colon cancer, allowing doctors to design personalized prevention plans.

Clinical diagnosis

In the image processing and interpretation for diagnosisAI offers algorithms that improve the quality and accuracy of clinical diagnostics. They allow to recognize complex patterns in image data automatically, to eliminate noise to increase their quality and to establish three-dimensional models from images of specific patients. In this field, we can highlight the research by IBM researchers on a new study on the use of a new AI model can predict the development of malignant breast cancer.

With rates comparable to those obtained by human radiologists, this algorithm can learn and make decisions about cancer development from imaging data and patient history. Specifically, it was able to predict the 87% of the analyzed cases and was also able to interpret the 77% of noncancerous cases. Therefore, this model could be a fundamental tool to help radiologists confirm or dismiss positive cases of breast cancer.

Personalized medical treatments

Another use of AI in medicine is to find personalized medical treatments for each patient. Based on a set of factors, such as medical history, lifestyle and genetics, the AI algorithms can analyze a large volume of genomic and biomarker data to identify patterns and risk factors.

This can be used to develop a specific medical treatment for the patient's needsThe use of AI in oncology, for example, helps to identify the best treatment for each type of cancer, taking into account the specific genetics of the tumor. For example, in oncology, AI helps to identify the best treatment for each type of cancer, considering the specific genetics of the tumor.

Health care

Patient care is one of the areas where AI can provide great support to both medical professionals and patients. In this case, the AI-based virtual assistants are an ideal solution for automating functions and tasks. These include the appointment management, the realization of basic health consultations, the symptom assessment and the administration of medications.

Promoting telemedicine

These systems have also enabled the evolution of telemedicine. In this sense, professionals can monitoring patients suffering from chronic diseases remotely and receive alerts of possible anomalies that may arise in their health condition. This offers wide-ranging benefits in reaching a larger number of patients, especially those who live in regions that do not have all the health services in their localities and must travel to receive medical care.

Resource management in medical centers and hospitals

Another area where AI can be implemented is in the management of material and human resources in clinics, hospitals and health centers. Examining large amounts of data from historical records can be essential for to foresee the resources required in a given situationThe company's management and optimization of the available resources can be very helpful for the management and optimization of the available resources. This can be of great help to avoid overcrowding of medical centers at times of high demand and be able to manage the inventory of medical supplies and the availability of beds and medications.

Drug research and development

Artificial intelligence has been fundamental in the development of medical research, both in the development of new drugs as in the optimization of clinical trials. The integration of artificial intelligence into drug design involves a multidisciplinary approach combining both chemistry and biology concepts as well as computer science. to accelerate the discovery of new treatments and medical solutions.

For this purpose, AI models created with machine learning and deep learning algorithms are used to analyze large amounts of data on chemical and biological compounds and the interaction between them.

Robotic surgery

Robotic surgery systems such as the Da Vinci use AI to perform complex surgical procedures with greater control and precision. These robots are controlled by the surgeons to make small incisions, which helps to reduce the margin of error, perform minimally invasive surgeries and improve patient recovery times..

Another key area in which artificial intelligence can be applied is in the creation of customized surgical plans. In this case, the following are used data from previous surgeries to optimize techniques and to predict possible complications. that may arise during operations.

Training

AI has a key role to play in the training of health professionals. It provides multiple tools that help medical specialists to acquire and perfect their skills in different areas, increasing their knowledge in a more efficient and personalized way.

On the one hand, the medical simulations through AI allow students to be able to implementing complex procedures and reducing the risk of errors. At the same time, the following stand out learning platforms that use AI to adjust educational content based on the level of knowledge of the learnerThe aim is to achieve greater efficiency in the learning process.

In summary, AI has a wealth of applications in medicine and there are new improvements and innovations every day that help to further advance the healthcare sector.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.

Diagnostic Imaging in Veterinary Medicine: Advances and Applications

by 4D Medical | Oct 10, 2024 | AI in medicine

The veterinary radiology has undergone significant progress in the last decade, radically transforming the way animal diseases are diagnosed and treated. With the introduction of advanced technologies and high-definition equipment, veterinarians are now able to obtain clear and detailed images that are crucial for a accurate diagnosis. This not only improves the quality of treatment, but also minimizes stress and discomfort for the animals during diagnostic procedures.

One of the most noteworthy innovations is the digitization of X-ray equipmentwhich allows instant viewing of images and facilitates faster and more accurate analysis. This technology not only speeds up the diagnostic process, but also reduces radiation exposure for both animals and operators, ensuring a safer and more efficient environment in veterinary clinics.

Main advances in veterinary diagnostic imaging

What advances have arisen in diagnostic imaging in the veterinary sector? We can highlight the following:

Use of CT and MRI in small animals

The use of CT in small animals has become an invaluable tool in veterinary radiology. This technique provides three-dimensional images of the inside of the animal's bodywhich is essential for accurate diagnosis of complex conditions. CT in small animals is especially useful for examining fine details of bones, internal organs and other structures that are difficult to visualize by conventional X-rays.

On the other hand, the veterinary magnetic resonance imaging offers significant advantages over other forms of diagnostic imagingespecially with regard to the soft tissue visualization. This method is extremely sensitive for detecting diseases of the brain, spinal cord and other internal organs in small animals. MRI does not use ionizing radiation, which makes it safe for repeated use for detailed follow-up and longitudinal diagnostics.

Advances in veterinary ultrasonography

Ultrasonography has revolutionized veterinary radiology applications, offering a new way of non-invasive diagnostic tool that is capable of to produce real-time images of internal organs. This technology is particularly useful for examining the abdomen and heart, as well as for reproductive management in a wide variety of animal species. With advances in transducers and improvements in image quality, ultrasonography allows veterinarians to perform precise diagnoses and procedures such as guided biopsies with high accuracy.

Innovations in veterinary cardiac ultrasound

Cardiac ultrasound has become an indispensable tool in veterinary medicine for the diagnosis and treatment of evaluation of the heart of the animals. This advanced technique enables veterinarians to observe the heart function in real time, offering a crucial window into the early detection of heart disease. Improvements in ultrasound machines and the development of more sensitive probes have significantly increased the accuracy of diagnoses, allowing earlier interventions and increasing the success rates of treating cardiac conditions in animals.

All these innovations, new trends and applications allow further progress in the veterinary sector to improve the diagnostics and treatments needed for all types of animals.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.

The Future of Medical Equipment: Trends and Developments

by 4D Medical | Oct 10, 2024 | AI in medicine

The medical technology is advancing at an unprecedented pace, driven by ongoing innovation in areas such as robotics, telemetry and digital diagnostic systems.. These advances are designed not only to improve the quality of medical care, but also to make it more accessible. 

Advances in medical technology

Innovations in X-ray equipment, for example, now make it possible to perform procedures faster and with less radiation exposure, benefiting both patients and healthcare professionals. What kind of advances are there in medical technology? The following is an overview of the latest news and developments in the healthcare sector:

New developments in X-ray equipment

The integration of artificial intelligence is enabling X-ray systems to provide preliminary diagnostic recommendations, which can accelerate the medical evaluation process and improve the accuracy of the early detection of diseases. 

Innovations in ultrasound and endoscopy equipment

In the field of ultrasonography and endoscopy, improvements in technology have led to the creation of more compact and portable devicesThe new system allows diagnostics to be performed in less conventional settings such as rural clinics or remote offices. The ultrasound systems are capable of providing high-resolution images that are essential for the diagnosis of musculoskeletal and cardiac conditions. At the same time, innovations in endoscopy are improving the ability of visualize and treat gastrointestinal conditions with less discomfort for the patient. 

Telemedicine

Telemedicine is another area that has shown great growth. It allows physicians to have remote consultations with patients and can monitor their health. Through the use of portable devicesspecialists will be able to perform diagnostics patients without having to be in person at a medical center. This is a major breakthrough, since facilitates access to medicine to more people, especially those living in less populated and remote regions.

Advances in high-field magnetic resonance imaging in veterinary medicine

High-field magnetic resonance imaging is revolutionizing veterinary diagnostics, particularly in small and exotic animals. This technology makes it possible to obtain detailed soft tissue imagingThis is crucial for neurological and soft-tissue diagnostics. With stronger magnetic fields, veterinarians are able to detect anomalies more accurately and quicklyThis will facilitate more accurate diagnoses and more effective treatments.   

Use of fluoroscopy in interventional veterinary procedures

Fluoroscopy is an imaging technique that makes it possible to view internal structures in motion in real time, being especially useful in veterinary interventional procedures. This technology is used to guide procedures such as stent placement, biopsies and minimally invasive surgeries. By providing continuous visualization, fluoroscopy helps to increase the precision of procedures and reduces the risks associated with surgical procedures. 

As we can see, there are more and more advances in the field of medical technology that help to make diagnoses and interventions faster, more effective and less risky for patients.

Kiko Ramos

CEO of 4D Médica. Expert in marketing and distribution of medical equipment.