Progenics Pharmaceuticals, Inc.

Exini Diagnostics Ab

Progenies Pharmacenticals, Inc.

Karl Vilhelm Sjöstrand

Jens Filip Andreas Richter

Aseem Undvall Anand

Lars Edenbrandt

Kerstin Elsa Maria Johnsson

Erik Konrad Gjertsson

Iman Hamadeh

Pierre Nordblom

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

Systems and methods for artificial intelligence-based image analysis for cancer assessment

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

Systems and methods for automated and interactive analysis of bone scan images for detection of metastases

Presented herein are systems and methods that facilitate user review and uploading of files comprising medical images and associated metadata from a local computing device to a network-based image analysis and/or decision support platform. The systems and methods described herein allow image upload to be performed in a secure fashion that prevents the network-based platform from accessing sensitive data as it is prepared for upload. Prior to file upload, sensitive data elements are flagged and their values removed and/or masked. Notably, the approaches described herein provide intuitive graphical user interface (GUI) tools that allow a user, such as a medical practitioner or researcher, to review not only the images and metadata in the files that they plan to upload, but also to review and control the process by which sensitive data elements are removed and/masked, thereby confirming that all files are free of sensitive information prior to upload.

Systems and methods for secure cloud-based medical image upload and processing

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, the systems and methods described herein offer improved interactive adjustment of intensity windowing for display of nuclear medicine images. The interactive intensity window selection tools described herein utilize a nonlinear scaling function that maps user adjustments to positions of displayed indicator widgets on a scale to intensity window thresholds. The form of the scaling function increasingly magnifies user adjustments at the upper range of the scale, but remains linear at the lower end. The intensity windowing tools presented herein allow the user to adjust intensity thresholds over a full range of intensities encountered in an image, up to the maximum value, while still preserving fidelity in an important range that includes lower intensities.

Systems and methods for interactive adjustment of intensity windowing in nuclear medicine images

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific organs and/or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.

Systems and methods for rapid neural network-based image segmentation and radiopharmaceutical uptake determination

The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.

Growing community of inventors

New York, NY, United States of America

Karl Vilhelm Sjöstrand