Preeti Cholleti’s Post

AI Advancement in Urology Medical Care. The impact of #AI in detecting prostate cancer is significant and continues to evolve. AI technologies, such as machine learning algorithms, have shown promise in analyzing medical imaging data (such as MRI or ultrasound images) to assist radiologists and clinicians in identifying potential signs of prostate cancer at an early stage. #AugmentedIntelligence refers to the collaboration between human healthcare professionals and advanced technology like #AI. In the context of medicine, Augmented Intelligence can enhance decision-making processes by providing insights from vast amounts of patient data that may not be readily apparent through traditional methods alone. Harvey Castro, MD, MBA. discussion likely emphasizes how these advancements can lead to more accurate diagnoses, personalized treatment plans, improved patient outcomes, and overall efficiency within healthcare systems. Additionally, he might explore the ethical considerations surrounding the use of AI in medicine while highlighting its potential for transforming various aspects of clinical practice. https://lnkd.in/eqzxytCN

AI Advancement in Urology Medical Care. The impact of #AI in detecting prostate cancer is significant and continues to evolve. AI technologies, such as machine learning algorithms, have shown promise in analyzing medical imaging data (such as MRI or ultrasound images) to assist radiologists and clinicians in identifying potential signs of prostate cancer at an early stage. #AugmentedIntelligence refers to the collaboration between human healthcare professionals and advanced technology like #AI. In the context of medicine, Augmented Intelligence can enhance decision-making processes by providing insights from vast amounts of patient data that may not be readily apparent through traditional methods alone. Harvey Castro, MD, MBA. discussion likely emphasizes how these advancements can lead to more accurate diagnoses, personalized treatment plans, improved patient outcomes, and overall efficiency within healthcare systems. Additionally, he might explore the ethical considerations surrounding the use of AI in medicine while highlighting its potential for transforming various aspects of clinical practice. https://lnkd.in/eqzxytCN

Learn how we're combining AI and radiology to detect pancreatic cancer in its earliest stages: https://lnkd.in/ecX6gMx

mlangles's Predictive AI unlocks the highest degree of accuracy in detecting and classifying brain tumors!    mlangles identifies and classifies brain tumors achieving 99% accuracy, resulting in more precise and prompt treatment. Connect with us to learn more about how mlangles elevates the Predictive AI game for brain tumor classification and advances healthcare! Visit our website @ mlangles.ai #mlangles #AI #innovation #healthcare #braintumor #cancerawareness

🚨 For those who are interested, here is the link to the tweetorial 🧵 for our study: https://lnkd.in/eaZ9p6pH

👉🏼 Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning's ability to provide diagnoses and treatment plans for example neuro-oncology cases 🤓 Giovanni Kozel 👇🏻 https://lnkd.in/e5XJjH2E 🔍 Focus on data insights: - ChatGPT-4 demonstrated an 85% accuracy in diagnosing example brain tumors and a 75% accuracy in providing treatment plans, outperforming ChatGPT-3.5 significantly. - The average scores given by twenty independent neurosurgeons rated ChatGPT-4's accuracy in diagnosis, treatment options, and treatment plans at 8.3, 8.4, and 8.5 out of 10, respectively. 💡 Main outcomes and implications: - ChatGPT-4 shows great potential as a diagnostic tool for brain tumors in neuro-oncology, with superior performance compared to ChatGPT-3.5. - The study highlights the importance of leveraging AI/ML technologies for accurate and efficient diagnoses and treatment planning in neuro-oncology cases. 📚 Field significance: - The findings underscore the transformative impact of AI/ML in enhancing healthcare outcomes and decision-making processes in neuro-oncology. - Integrating advanced AI tools like ChatGPT-4 can lead to improved patient care, streamlined workflows, and better treatment outcomes in neurosurgical practice. 🗄️: [#ArtificialIntelligence #MachineLearning #NeuroOncology #Healthcare #AIinMedicine]

📣 I am happy to share our new publication "Evaluation of a Cascaded Deep Learning–based Algorithm for Prostate Lesion Detection at Biparametric MRI" https://lnkd.in/ev2Yzc28 🔵 Multiparametric MRI (mpMRI) of the prostate enhances cancer visualization and facilitates targeted biopsies, improving diagnosis accuracy and reducing unnecessary procedures. The Prostate Imaging Reporting and Data System (PI-RADS) guidelines standardize mpMRI interpretation, yet variations persist in reproducibility and performance. Artificial intelligence (AI) algorithms, particularly deep learning models, show promise in augmenting radiologist performance by standardizing lesion detection and segmentation. In this study we aim to assess the performance of a cascaded deep learning AI algorithm in detecting and segmenting prostate cancer lesions, comparing it with expert radiologists and biopsy results within a prospective PI-RADS evaluated patient population. 👍 Congratulations to Yue Lin, Mason Belue Enis C. Yilmaz Stephanie Harmon Yan Mee Law and our Team 🎉 #ProstateCancer #ProstateMRI #ArtificialIntelligence #AI #CancerResearch #TeamScience

✨ Exciting News! ✨ I'm thrilled to share that one of our research articles titled "TransResUNet: Revolutionizing Glioma Brain Tumor Segmentation through Transformer-Enhanced Residual UNet" has been accepted for publication in IEEE Access, with an Impact Factor of 3.4. This work dives into the potential of Transformer-based architectures combined with Residual UNet for more accurate and efficient segmentation of glioma brain tumors, pushing the boundaries of AI in healthcare and contributing to the advancement of medical imaging. #AI #Healthcare #Research #IEEEAccess #BrainTumorSegmentation #MedicalImaging #Transformers #UNet #DeepLearning #AIinHealthcare #GliomaResearch

Exciting advancements from Phedias Diamandis at University Health Network ! His work combines AI and neuropathology to revolutionize glioblastoma research and patient care. Tools like HAVOC and PHARAOH personalize medicine using AI innovations. #AIHealthcare #Neuropathology #Innovation #medtech #patientcare #diagnostics #pathology #cancer

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI Link: https://lnkd.in/dgF8ATrx A deep learning (DL) model was developed to predict clinically significant #ProstateCancer (csPCa) using multiparametric MRI 🩻data from 5735 examinations in 5215 patients, without needing specific tumor location information ➡️The DL model, trained on various MRI sequences, demonstrated similar performance to radiologists🥼, with areas under the receiver operating characteristic curves (AUCs) of 0.89 for both on the internal test set and 0.86 and 0.84 on the external test set, respectively. ➡️Combining the DL classifier with radiologists improved the AUC to 0.89. Gradient-weighted class activation maps (Grad-CAMs) effectively highlighted csPCa lesions, supporting the model's accuracy. Thus, the DL model performed comparably to radiologists in detecting csPCa, offering a reliable and consistent diagnostic tool. Shiba K. Candice Bolan Akira Kawashima Lance Mynderse Mitchell Humphreys Panagiotis Korfiatis Pouria Rouzrokh, MD, MPH, MHPE Alex Bratt Gian Marco Conte Bradley Erickson