Published on: February 17, 2025 / update from: February 17, 2025 - Author: Konrad Wolfenstein
The role of artificial intelligence in healthcare: personalized treatments, diagnostic support and prediction of animal movements - Image: Xpert.digital
Transformation through AI in the body & cosmos: how algorithms heal heart defects & count whales
AI as a key technology in healthcare and species protection: artificial intelligence as a game changer
Artificial intelligence (AI) is no longer just a catchphrase from science fiction films, but a reality that penetrates our lives in many ways. Especially in the healthcare system and in the area of species protection, KI unfolds enormous potential that revolutionizes traditional methods and opens up completely new ways. We are at the beginning of an era in which AI not only serves as a supporting tool, but also acts as a driving force for innovation and progress. This report illuminates how AI already makes a decisive difference in three central areas-the personalized treatment of atrial fibrillation, the AI-based diagnosis in digital pathology and the prediction of animal movements to protect marine ecosystems and promises even greater changes in the future.
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Personalized treatment of atrial fibrillation by AI: a paradigm shift in cardiology
Attachment fibrillation, the most common cardiac arrhythmia affects millions of people worldwide and represents a significant burden on the health systems. Treatment of this complex disease is often challenging because it can be very different from patient to patient. This is where AI comes in and enables fundamental change towards personalized therapy approaches.
AI-optimized ablation procedure: precision and effectiveness at a new level
A particularly promising area is catheter ablation, a minimally invasive procedure for the treatment of atrial fibrillation. With this method, pathological heart fabric that causes rhythm disorders is targeted. Traditionally, the ablation was often based on a rather standardized, anatomically oriented approach. But the Tailored Af study, a milestone in interventional cardiology, has shown how AI can significantly improve the precision and effectiveness of this procedure.
In this randomized, controlled study, some of the patients used AI-based technology called Volta AF-Xplorer ™. This system analyzed over 5,000 data points per second in real time and identified spatial and time-time dispersal electrograms-a complex pattern of electrical signals that indicates pathological heart muscle areas. Compared to the control group, in which the ablation was carried out according to conventional methods, the AI-based cohort showed impressive results. After 12 months, 88 % of the patients were free of arrhythmias in the AI group, while the control group was only 70 %. In addition, acute recurrences occurred much less frequently in the AI group (15 % vs. 66 %). These results clarify that AI is able to intraoperatively process an enormous amount of data and thus enable more precise and individualized treatment.
The name "Ablation" comes from Latin and means something like "take away" or "remove". In medicine, it describes the targeted removal or destruction of tissue. In addition to the catheter deflation in cardiac arrhythmias, there are numerous other areas of application, such as tumor ablation, in the tumor tissue by heat, cold or other methods, or endometrium ablation that is used to treat certain gynecological diseases. Catheter ablation has established itself as one of the most important therapy options for atrial fibrillation in recent years and is now even more effective and safer thanks to AI-based procedures.
Predictive models for therapeutic successes: risk profiles and personalized forecasts
Another promising approach in the field of AI-based atrial fibrillation therapy is the development of predictive models. The accelerates project under the direction of the Leipzig heart center works on machine learning models that can create individual risk profiles using 12-channel ECG data. These models go far beyond the pure prediction of recurring atrial fibrillation after ablation. They are also able to recognize left -wing atrial remodeling - a fibrotic conversion process of the left atrium, which not only favors the development of atrial fibrillation, but is also accompanied by a significantly increased risk of stroke. Studies show that left-wing atrial remodeling can increase the risk of stroke by 3.2 times.
In order to maximize the prediction accuracy of these models, register data from over 100,000 ablations (as of 2021) are integrated. The results are impressive: the models achieve a predictability of 89 % for so-called low-voltage areas in the heart, i.e. areas with reduced electrical activity, which often correlate with fibrotic tissue. Compared to conventional risk cores used in clinical practice, the AI-based models exceed them by 23 %. This means that AI is able to identify patients who have a particularly high risk of recurring atrial fibrillation or for strokes, and thus enabling personalized therapy planning. In the future, such predictive models could help doctors choose the optimal treatment strategy for each individual patient and thus maximize the therapy success.
Pulsed-Field-Ablation (PFA): The next generation of ablation technology
In addition to the optimization of existing replacement techniques, KI also drives the development of completely new procedures. An example of this is the Pulsed Field Ablation (PFA), an innovative technology that uses electrical pulse to selectively desolate heart muscle cells. In contrast to conventional ablation methods based on heat or cold, PFA works with ultra -shorts, high -frequency electrical fields. This leads to a very targeted necrosis of cardiac muscle cells, while surrounding tissue, such as the esophagus or the phrenic nerve, is spared.
AI plays a crucial role in PFA by adapting the pulse frequency to the tissue thickness in real time. This ensures an optimal replacement effect with maximum security. First studies at the German Heart Center Berlin (DHZC) show promising results. The procedural period could be reduced by up to 40 % by using PFA compared to conventional replacement procedures. At the same time, a high security of the procedure was demonstrated, especially with regard to the protection of the esophagus and the phrenic nerve, which can sometimes be damaged in conventional ablation methods. PFA could therefore not only make the ablation of atrial fibrillation more efficient, but also safer and make the treatment more pleasant for patients.
AI in digital pathology and diagnostic support: precision and speed in the service of the diagnosis
Pathology, the teaching of the diseases, plays a central role in medical diagnostics. Traditionally, pathological diagnostics are based on the microscopic examination of tissue samples. This process is time -consuming, subjective and can be influenced by human fatigue and variability. The digital pathology, i.e. the digitization of tissue and the use of computer -aided analysis methods, promises a revolution here. AI is a key factor to fully use the digital pathology and to raise the diagnosis to a new level.
Automated tumdetection: Channel cells recognize with deep learning
A central scope of AI in digital pathology is automated tumor. The Fraunhofer Institute for microelectronic circuits has developed Deep learning algorithms, which can identify malignant cell cluster with impressive precision in digitized tissue slices. The sensitivity of these algorithms is 97 %, which means that they recognize existing tumor cells in 97 % of cases.
By using transfer learning, a method of machine learning, in which knowledge is transferred from one task to another, the system could be trained on a huge database of 250,000 histopathological images. This enables the system not only to recognize tumor cells, but also to differentiate between 32 subtypes of the duktal breast cancer, the most common form of breast cancer. This detailed subtyping is of crucial importance for therapy planning. In addition, the AI can shorten the diagnosis period in pathology by up to 65 %, which leads to a faster diagnosis and thus to an earlier start of therapy for the patients. Automated tumor detection by AI can thus significantly improve the efficiency and accuracy of pathological diagnostics and at the same time reduce the workload for pathologists.
Neural networks in routine pathology: Find out micrometastases that have been overlooked
Another example of the successful use of AI in the pathology is the work of the company Aisencia, the Convolutional Neural Networks (CNNS). These special neuronal networks are particularly good at recognizing patterns in pictures and are used in digital pathology, for example to predict microvascular invasions in colon carcinoma. Microvascular invasions, i.e. the penetration of tumor cells into the smallest blood vessels, are an important prognostic factor in colon cancer and provide information about the risk of metastasis.
In a validation study on 1,200 samples, the Aisencia AI achieved 94 % with the assessment by experienced pathologists. This shows that the AI is able to recognize microvascular invasions with a similar accuracy as human experts. However, it is noteworthy that the AI in this study detected an additional 12 % micrometastases that were overlooked during the initial assessment. This underlines the potential of AI to recognize subtle patterns and details that may escape the human eye. The use of CNNs in routine pathology can thus improve the quality of the diagnostics and contribute to the fact that no important information is overlooked.
Saturn: AI-based diagnosis of rare diseases-put an end to the diagnostic andS lake
Rare diseases are a special challenge for the health system. Often years pass until patients with a rare illness receive the correct diagnosis. These so-called "diagnostic ands lakes" are very stressful for those affected and their families. Here AI can make an important contribution to accelerate and improve the diagnosis.
The smart doctor portal Saturn is an example of a AI-based system that combines natural language processing (NLP) with knowledge graphs in order to generate differential diagnoses from symptom lists. NLP enables the AI to understand and process natural language, while knowledge graphs represent medical information and relationships in a structured form. In the pilot phase of the project, Saturn was tested on the diagnosis of rare metabolic diseases. The system correctly recognized 78 % of cases of Gaucher's disease and 84 % of the mucopolysaccharidosis. The misclassification rate was only 6.3 %.
A special advantage of Saturn is the connection to the SE-Atlas, a directory of specialized treatment centers for rare diseases. This allows the system not only support the diagnosis, but also suggest suitable experts and centers directly. This can significantly shorten the time until the correct diagnosis and treatment. Studies show that Saturn can reduce the diagnosis period from an average of 7.2 years to 1.8 years. AI-based diagnostic support systems such as Saturn have the potential to fundamentally improve the care of patients with rare diseases and to save them unnecessary suffering.
Prediction of whale movements using AI-based satellite analysis: species protection in the 21st century
Ki plays an increasingly important role not only in healthcare, but also in species protection. Monitoring and the protection of endangered animal species are crucial for the preservation of biodiversity. Traditional methods for animal observation are often time -consuming, expensive and it is difficult to cover large areas. AI-supported satellite analysis and acoustic monitoring open up completely new opportunities to grasp animal movements over a large area and thus to make species protection more effective.
SpaceWhale: Deep Learning for Marine Megafauna - whales count from space
The SpaceWhale system developed by Bioconsult SH is an impressive example of how AI and satellite technology can be combined in order to monitor marine megafauna. SpaceWhale analyzes satellite images with an extremely high resolution of 30 cm (provided by Maxar Technologies) using an ensemble made of CNNS and Random-Forest models. These AI models are trained to recognize and classify whales in satellite images.
Spacewhale was successfully used in the Bay of Auckland, an important habitat for southern Glattwhales (Eugbalaena Austria). The AI detected 94 % of the whales that were in the area. The manual validation by experienced naval biologists confirmed the high accuracy of the system with 98.7 %. SpaceWhale reduces the cost of waler recording compared to conventional aircraft counts by up to 70 %. In addition, the method enables large -scale inventory surveys in the Hochsee for the first time, i.e. in areas that are difficult to access with conventional methods. SpaceWhale shows how AI-based satellite analysis can revolutionize species protection by offering more precise, cheaper and large-scale surveillance options.
Acoustic monitoring and habitat modeling: Listen whales and predict hiking routes
In addition to visual recording by satellite images, acoustic monitoring also plays an important role in species protection. The Whalesafe project before California combines hydrophone data (underwater microphones) with AI-based LStM networks (Long Short-Term memory) to predict the presence of blue whales in real time. LStM networks are a special type of neuronal networks that are particularly good in recognizing time connections in data.
In addition to the acoustic data, the Whalesafe models also take into account environmental factors such as sea temperature, chlorophyll a concentration (an indicator of algae blossom and thus for food availability) and ship traffic data. By combining these different data sources, the models achieve an impressive hit rate of 89 % when predicting blue whale hiking routes. A central goal of Whalesafe is the reduction of ship collisions, one of the main threats for whales. The collision rate in the Santa Barbara Canal was already reduced by 42 % by automatic warnings to ships that enter critical areas. Whalesafe demonstrates how AI-supported acoustic monitoring and habitat modeling can contribute to better protect whales and other marine animals and minimize human-animal conflicts.
Real time detection of communication signals: Understand the language of the sperm whales
A particularly fascinating and future-oriented project in the field of AI-based species protection is the Cetacean Translation Initiative (Ceti). Ceti has set itself the goal of deciphering the communication of sperm whales. Pottwhales are known for their complex clicks, so -called "codas", which they use for communication with each other. The Ceti project analyzes over 100,000 hours of sperm whale clicks using a transformer models. Transformer models are a state-of-the-art architecture of neural networks that has proven to be particularly efficient in language processing in recent years.
The AI of Ceti from Ceti recognizes context -specific codas through contrastive learning, a method of mechanical learning, in which the AI learns to distinguish similar and unlocked data. These codas are used, for example, when coordinating dives or young breeding. Initial results indicate that Pottwal-Communication has a syntax with recurring 5-element sequences. These findings could enable conclusions about intentional communication, i.e. that sperm whales are able to communicate consciously and in a targeted manner. Ceti is an ambitious project that not only revolutionize our understanding of Wal communication, but also open up new ways for species protection by enabling us to better respond to the needs and behaviors of these fascinating animals.
Key technology for a better future
The examples in this report impressively show that the integration of AI into healthcare and species protection already has a transformative effect. In cardiology, AI enables more precise and personalized merging methods, accelerates and improves tumor diagnosis in pathology, and in species protection it revolutionizes the monitoring marine species and enables a deeper understanding of complex animal behavior. But this is just the beginning.
Future fields such as Quantum Machine Learning, which could use the immense computing power of quantum computers, promise further breakthroughs in arrhythmia forecasts and other medical areas. In species protection, swarm intelligence-based systems that reproduce the collective behavior of insect swarms or swarms of birds could be used for whale persecution and the protection of entire ecosystems. In order to exploit the full potential of AI-based innovations, however, close interdisciplinary cooperation between medicine, computer science, ecology and many other disciplines is essential. Only through the exchange of knowledge and expertise can we ensure that AI technologies are used responsibly and for the benefit of people and the environment. The future is intelligent - we shape it together.
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