Ai Art: Offsetting Strokes For Creative Masterpieces

Artificial intelligence (AI) has been applied to the diagnosis, treatment, and rehabilitation of stroke victims. AI technology has been used to aid in various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, and large vessel occlusion detection. AI can also be used to predict imaging and clinical outcomes after ischemic stroke, as well as to predict which patients will suffer from depression and cognitive dysfunction after strokes. AI-based systems to guide stroke treatment decisions have been shown to improve stroke care quality and lead to fewer recurrent strokes, heart attacks, and vascular death among stroke survivors.

How to create editable offset text in Adobe Illustrator

Yes, it is possible to create editable offset text in Adobe Illustrator. Here is a step-by-step guide:

• Open Adobe Illustrator and create a new file or open an existing one.
• Use the Type tool (T) to write your desired text on the Artboard.
• In the Properties window, choose the font, size, and fill color for your text. You can also format the text by adjusting paragraph alignment, kerning, and text styles.
• It is recommended to use a more legible font type, like a sans serif, for better results.
• Go to the Appearance panel. If it is not open, go to Window > Appearance to open it.
• In the Appearance panel, click on "Add New Stroke" at the bottom. You can now choose the stroke color. Decide on the stroke color carefully as it is harder to change later compared to the fill color.
• In the Appearance panel, go to "Add New Effect" > "Offset Path". This will create a gap between the stroke and the filled text.
• In the Offset Path popup, adjust the gap spacing in the Offset box to your desired thickness. For example, you can use 10px.
• In the Joins box, choose the outline style. You have three options: Miter, Round, or Bevel. The Round option creates a bubble text effect, while Miter or Bevel give a more block-like outline.
• Set the Miter limit to 4 and adjust any other settings as desired.
• Click "OK" when you are happy with the spacing and joins of the outline.
• At this point, you will notice that the outlines go around each individual letter. To combine them into one outline per word, go to Effects > Pathfinder > Add in the Appearance panel.
• Now you can use the Type tool to edit your text as needed while keeping the offset effect. You can change the text, add bold or italic effects, or adjust paragraph alignment.

By following these steps, you can create editable offset text in Adobe Illustrator, allowing you to make further changes to your text while maintaining the offset effect.

AI-based system to guide stroke treatment decisions

An AI-based system to guide stroke treatment decisions has the potential to improve patient outcomes and reduce the chances of recurrent strokes, heart attacks, and vascular deaths among stroke survivors.

AI technology can aid neurologists by facilitating the sharing of information and combining the strengths of humans and machines. AI can be applied to the interpretation of imaging studies, such as CT and MRI scans, to quickly and accurately detect abnormalities and guide treatment decisions.

A clinical trial called GOLDEN BRIDGE II compared the effectiveness of AI-based evaluation and treatment for ischemic stroke patients with standard care provided by stroke teams in 77 hospitals in China. The AI system integrated participants' brain imaging scans, interpreted by AI, with established clinical knowledge for stroke diagnosis, classification, and guideline-recommended treatment.

The use of AI-based systems in stroke treatment has been found to improve the quality of stroke care and reduce the risk of new vascular events. This technology can also be used to assist with stroke rehabilitation, providing guidance to improve motor and gait function and help treat speech, language, and vision problems.

While AI has shown promising results in guiding stroke treatment decisions, there are some limitations and challenges. These include the black box problem, where it is not always clear how and why a decision has been made by the AI system, and the need to constantly update the AI models to reflect changes in clinical guidelines and disease prevalence.

Overall, AI-based systems have the potential to revolutionize stroke treatment by providing timely and data-driven recommendations to improve patient outcomes and reduce the risk of recurrent vascular events.

AI and the evaluation and treatment of stroke

Stroke is a leading cause of disability and death worldwide, with an annual mortality rate of about 5.5 million. In the United States alone, stroke affects close to 800,000 people each year, costing nearly $56.5 billion in health care services, medicine, and missed workdays.

Artificial intelligence (AI) has emerged as a promising tool for the early identification and management of acute stroke, including ischemic and haemorrhagic subtypes. AI can assist in various aspects of the stroke treatment paradigm, such as infarct or haemorrhage detection, segmentation, classification, large vessel occlusion detection, and prognostication.

One of the critical applications of AI in stroke evaluation and treatment is through the use of machine learning (ML) algorithms. ML is a subfield of AI that uses data to make predictions or decisions without being explicitly programmed. Supervised learning, unsupervised learning, and reinforcement learning are the three primary subdivisions of ML. Supervised learning uses labelled "training" data to predict labels for new "test" data, while unsupervised learning identifies patterns and clusters in unlabeled training data. Reinforcement learning, on the other hand, involves an agent interacting with its environment and learning from the rewards and penalties it receives to optimize its behaviour.

Deep learning (DL) is a relatively new type of supervised learning that replicates the human brain's functioning using artificial neural networks (ANNs). ANNs are composed of multiple connected nodes or "neurons," with an input layer, hidden layers, and an output layer. This technology enables systems to perform complex tasks such as image recognition, which is particularly useful for analyzing computerized tomography (CT) and magnetic resonance imaging (MRI) scans to detect abnormalities like acute cerebral ischemia or haemorrhage.

AI has been applied to the interpretation of imaging studies, which is crucial for stroke management. Automated imaging analysis using DL methods can speed up CT and MRI interpretation and assist in areas where neuroradiologists may not be readily available. Additionally, AI can aid in the detection of intracranial haemorrhage and large vessel occlusion, helping clinicians make timely decisions about thrombolytic and neuro-interventional therapy for acute cerebral ischemia.

AI models can also evaluate clinical, laboratory, and imaging data to provide individualized recommendations for stroke treatment and rehabilitation. Robotic devices utilizing AI can analyze a patient's movement patterns and provide guidance to improve motor and gait function. Furthermore, AI may assist in developing programs to address speech, language, and vision problems following a stroke.

Despite the promising applications of AI in stroke evaluation and treatment, there are some limitations. One significant challenge is the "black box problem," where the decision-making process of certain ML algorithms, especially deep neural networks, is not transparent. This lack of transparency can hinder confidence in AI systems. Additionally, the accuracy of AI models may decrease over time if they are not updated to reflect changes in disease prevalence and other factors.

In conclusion, AI holds tremendous potential to assist medical professionals in the care of stroke patients. As technology advances and more data becomes available, AI will likely become increasingly accurate and helpful in the diagnosis, evaluation, treatment, and rehabilitation of stroke, ultimately improving patient outcomes.

AI and acute stroke imaging

Artificial intelligence (AI) has a wide range of applications in acute stroke imaging, including ischemic and haemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. AI can help with various aspects of the stroke treatment paradigm, including infarct or haemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score (ASPECTS) grading, and prognostication.

AI technology is a rapidly growing field, providing a promising avenue for fast and efficient imaging analysis. AI applications for imaging of acute cerebrovascular disease have been implemented, including tools for triage, quantification, surveillance, and prediction. This technology also assists with stroke rehabilitation, with robotic devices using AI to analyse a patient's movement patterns and provide guidance to improve motor and gait function.

AI uses computers to mimic human intelligence. Machine learning (ML) is a subset of AI, and deep learning (DL) is a subset of ML. DL is distinguished by its ability to independently learn abstract, high-order features from data without requiring feature selection. Artificial neural networks (ANNs) are a subtype of DL that mimic biological neurons and are composed of an input, one or more hidden layers, and an output. ANNs can be used to "de-noise" perfusion images to give clearer pictures and reduce radiation doses for CT scans.

Convolutional neural networks (CNNs) are a type of DL model that has been particularly successful and popular for image classification in medical imaging. CNNs have been used to detect pulmonary nodules, colon cancer, and cerebral microbleeds. They have also been applied to CT angiography in the detection of acute ischemic stroke, achieving high sensitivity, specificity, and accuracy.

The evaluation of AI performance includes metrics such as sensitivity, specificity, accuracy, and the area under the curve (AUC). While AI has many benefits, there are also some limitations to its use in stroke imaging. One challenge is the need for large, organised, well-classified, and accurate datasets for ML algorithms to perform optimally. Additionally, interpretability is often difficult, especially for DL algorithms, leading to the "black box" problem. This lack of transparency can hinder confidence in AI systems.

How to offset a text stroke in Illustrator

Open a new file or find the file you wish to add your text to:

Open Adobe Illustrator and create a new file or open an existing one that you want to add text to.

Write your text on the Artboard:

Use the Type tool (T) to write your text on the Artboard. You can choose your desired font, size, and fill color in the Properties window. Other formatting tools such as paragraph alignment, kerning, and text styles can also be adjusted at this point.

Add a stroke to your text:

Go to the Appearance panel (Window > Appearance) and click on "Add New Stroke" at the bottom. If the stroke color is the same as your text fill, you may not notice a difference. However, if you choose a different color stroke, it will appear around the outside of your text.

Offset your text stroke:

In the Appearance panel, go to "Add New Effect > Offset Path." This will create a gap between the stroke and the filled text. In the Offset Path popup, you can adjust the gap spacing in the Offset box to your desired width.

Choose the style of your outline:

In the Offset Path popup, you can also choose the style of your outline in the "Joins" box. There are three options: Miter, Round, or Bevel. The Round option creates a bubble text effect, while Miter or Bevel will give a more block-like outline.

Combine in Pathfinder:

At this point, you will notice that the outlines go around each individual letter, causing the lines to overlap between letters in a word. To fix this, go to the Appearance panel, then Effects > Pathfinder > Add. This will merge the overlapping lines into one outline per word, creating a smooth bubble outline if you chose the Round option.

Write with editable offset text:

The text remains fully editable, allowing you to change or add text while keeping the offset effect. You can use the Type tool to edit your text, and the effect will stay in place. You can even modify other type settings like bold, italic, and paragraph alignment.

Turn your offset text into a vector:

Once you are happy with your offset text, you can convert it into a vector for other uses. Right-click on the text and go to "Create Outlines," then "Object > Expand Appearance." To edit your vectorized text, right-click on the expanded text and select "Ungroup" to individually select and modify different aspects of your design.

Frequently asked questions