WSK Medical’s Post

October is Breast Cancer Awareness Month – Let's Talk About the Future of Breast Cancer Care. With the growing role of AI in breast cancer detection and treatment, we're entering a new era of care where early detection and personalized treatment are more achievable than ever before. AI for Early Detection: AI-enhanced tools can analyze mammograms with unprecedented precision, helping detect cancer earlier, when it’s most treatable. Personalized Treatment: AI-powered systems analyze genomic data to design individualized treatment plans, targeting cancer at its core. Monitoring & Prognosis: AI models can predict outcomes and track responses to treatment, allowing for real-time adjustments that enhance effectiveness. As we continue to spread awareness about breast cancer, let’s also champion the cutting-edge technologies that are saving lives. We can empower patients and improve outcomes. 💪💗 #BreastCancerAwarenessMonth #AI #BreastCancer #HealthcareInnovation #CancerTreatment #EarlyDetection #PrecisionMedicine

🎗️ Breast Cancer Awareness Month: Innovating for Early Detection 🎗️ In recognition of Breast Cancer Awareness Month, I'm excited to share a project I developed a Breast Cancer Detection System utilizing Logistic Regression to support early diagnosis. By leveraging patient data and predictive modeling, this system classifies tumors as benign or malignant, providing a valuable tool for medical practitioners. This project emphasizes the importance of data-driven approaches in healthcare, where timely and accurate detection can make a significant difference. With machine learning, we can improve diagnostic accuracy, enhance patient care, and contribute to the global fight against breast cancer. Let’s continue raising awareness and working towards a world with early detection and effective treatments for everyone. 💖 #BreastCancerAwareness #MachineLearning #LogisticRegression #HealthcareInnovation #Datascience

🎗️ Breast Cancer Detection Using Logistic Regression Breast cancer is a major health challenge, and early detection is critical. In this project, I built a Logistic Regression model to classify breast tumors as malignant or benign using the Kaggle Breast Cancer dataset. ✨ Highlights: ✅ Simplicity: Logistic Regression offers a robust yet interpretable solution. 📊 Data Preprocessing: Scaled features for optimal convergence and balanced the dataset. ⚙️ Evaluation: Achieved high accuracy and F1-score, ensuring reliable predictions. 🌍 Impact: Provides a fast, accessible approach for breast cancer screening. 💡 What I Learned: 1. Developed strong skills in data cleaning, feature scaling, and model evaluation. 2. Gained insights into the importance of simple, interpretable models in healthcare. #LogisticRegression #BreastCancerDetection #AIForGood #MachineLearning

🔍 Early Detection of Breast Cancer with Machine Learning 📊 Proud to share my latest project aimed at improving breast cancer diagnosis using a Support Vector Machine (SVM) model. Given the global prevalence of breast cancer, early detection is crucial for effective treatment and survival. Traditional diagnostic methods often require significant time and resources, with some limitations in detecting cancer at its earliest stages. Project Objective 🎯 This project leverages morphometric and textural features from radiographic data to train a predictive SVM model that differentiates between benign and malignant tumors. Using R with an SVM and a radial basis function (RBF) kernel, the model achieves high accuracy and interpretability, offering a cost-effective, efficient, and scalable tool that can integrate into healthcare systems. Key Takeaways: Model Type: SVM with RBF kernel, capturing complex patterns in diagnostic data. Impact: Enhances diagnostic precision and supports early screening in clinical settings. #MachineLearning #BreastCancerAwareness #HealthcareInnovation #DataScience

Did you know that lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death overall and in men worldwide, with almost 2.5 million cases (1 in 8 cancers) and 1.8 million deaths (1 in 5 deaths)? — American Cancer Society (2024) A study from the University of Cologne, led by Dr. Yuri Tolkach and Professor Reinhard Büttner, has unveiled a revolutionary AI-based digital pathology platform that automates the analysis of lung cancer tissue samples. This innovative technology speeds up diagnosis and accuracy, allowing pathologists to extract critical genetic information for personalised treatment options. The study, published in Cell Reports Medicine, highlights how this platform could pave the way for new clinical tools that monitor patient responses to therapy. As we embrace digital transformation in healthcare, this advancement signifies a great leap towards improving patient outcomes. Let's celebrate this remarkable achievement and its potential impact on lung cancer treatment! #LungCancer #DigitalPathology #AI #HealthcareInnovation Follow Remy Takang Arrey, CAPA, LL.M, MSc.

💡 Breakthrough in Breast Cancer Care: a new AI Model predicts Cancer Severity 💡 A cutting-edge AI model is now helping doctors to predict more accurately the severity of breast cancer. This technology is potentially sparing thousands of patients from unnecessary treatments. It does so by analyzing critical data – like tumor size, growth patterns, and genetic markers – to accurately assess cancer severity. With this data analysis, this AI tool can support doctors in various ways: 🎀 personalizing treatment plans 🎀 reducing patient stress 🎀 focusing resources where they’re needed most This is a huge leap forward in precision medicine and cancer care 👩⚕️🔬. Discover how AI is reshaping the future of breast cancer care: https://fcld.ly/j9j7457 #AIInHealthcare #BreastCancerAwareness #PrecisionMedicine #automatica

What’s pushing breast cancer detection tech forward? AI that sees what others might miss. iCAD’s latest breakthrough, ProFound Detection Version 4.0, has just received FDA clearance, raising the bar in breast cancer screening. • Enhanced Detection: 22% better at catching aggressive cancers • Precision for Dense Tissue: 50% improvement in dense breast cancer detection • Reduced False Positives: Precision without the noise As Dana Brown, iCAD’s CEO, says, "This fourth-generation AI solution not only enhances detection but also supports clinicians with precise, efficient diagnostics." With these improvements, iCad’s solution supports radiologists in detecting even the toughest cases, setting a new benchmark in early and accurate breast cancer detection. #medtech #AI #icad #breastcancer

🔬 Researchers from MIT and ETH Zurich have made a breakthrough in breast cancer diagnostics with a new AI model that can identify various stages of ductal carcinoma in situ (DCIS), a preinvasive breast tumor, using simple tissue images. 👨🔬 The Details: • Comprehensive Analysis: The AI model examines chromatin images from 560 tissue samples (122 patients) to identify 8 distinct cell states across different DCIS stages. • Advanced Insights: By analysing both cellular composition and spatial arrangement, the model highlights the importance of tissue organization in predicting disease progression. • Early Detection: Interestingly, cell states associated with invasive cancer were found even in tissue that appeared normal. 🚨 Why It Matters: This innovative AI model has the potential to revolutionize breast cancer diagnostics by providing a more accessible, cost-effective, and quicker method to assess DCIS risk. While clinical validation is still ongoing, this technology is expected to collaborate closely with pathologists in the near future, enhancing early and accurate cancer detection. 💥 #AI #HealthcareInnovation #BreastCancerAwareness #MedicalResearch #MIT #ETHZurich #HealthTech #CancerDiagnostics #AIinHealthcare #FutureOfMedicine

Celebrating this month helps remind everyone of the importance of regular screenings and staying informed about the latest innovations in healthcare. Let’s use this month to further our commitment to improving clinical outcomes and supporting those affected by breast cancer. Invenia™ ABUS 2.0 is a great example of how advanced technology can significantly enhance early breast cancer detection, especially in women with dense breast tissue where traditional mammography might be less effective. This innovative ultrasound solution, combined with AI, provides a non-invasive, patient-friendly approach that improves diagnostic accuracy. The AI Assistant helps radiologists by identifying potential areas of concern more efficiently, which can lead to earlier and more accurate detection of breast cancer. This aligns perfectly with GE HealthCare’s mission to advance technology for better clinical outcomes. Learn more https://lnkd.in/eZQiDGcF #BCAM2024 #breastcare #breastcancer #ultrasound #AI #densebreasts #3DABUS #mammography

Would you like to know if you might develop breast cancer in the next 5 years? Stay tuned...... A new AI system, AsymMirai, can predict whether a woman will develop cancer in the next 1-5 years based solely on localized differences between her left and right breast tissue. It has a 66% success rate in identifying future cancer cases. The study's coauthor imagines a day when radiologists could use the model to help develop personalized screening strategies. Doctors might advise those with higher scores to get screened more often, supplement mammograms with an MRI , and keep a close watch on trouble spots identified by AI. While AI has the potential to improve breast cancer detection and save lives, there is still caution among experts. It will likely still be some time before doctors and patients are comfortable relying solely on AI algorithms. You can read more here https://lnkd.in/gidEHAyZ