An MR Spectroscopy (MRS) system and approach is provided for measuring spectral information corresponding with propionic acid (PA), either alone or in combination with other measurements corresponding with other chemicals, to diagnose and/or monitor at least one of bacterial infection, such as associated with P. acnes, or conditions related thereto such as nociceptive pain associated with tissue acidity. An interfacing DDD-MRS signal processor receives output signals to produce a post-processed spectrum, with spectral regions corresponding with certain chemicals, including PA, then measured as biomarkers. A diagnostic processor derives a diagnostic value for each disc, and performs certain normalizations, based upon ratios of the spectral regions related to chemicals implicated in degenerative painful tissue pathology, such as PA and hypoxia markers of lactic acid (LA) and alanine (AL), and structural chemicals of proteoglycan (PG) and collagen or carbohydrate (CA).

A monitoring apparatus for a human body includes a node network with at least one motion sensor and at least one acoustic sensor. A processor is coupled to the node network, and receives motion information and acoustic information from the node network. The processor determines from the motion information and the acoustic information a source of acoustic emissions within the human body by analyzing the acoustic information in the time domain to identify an event envelope representing an acoustic event, determining a feature vector related to the event envelope, calculating a distance between the feature vector and each of a set of predetermined event silhouettes, and identifying one of the predetermined event silhouettes for which the distance is a minimum.

Described are computer-based methods and apparatuses, including computer program products, for implant planning for multiple implant components using constraints. A representation of a bone and a representation of a first implant component are displayed with respect to the representation of the bone. A representation of a second implant component is displayed, wherein the first implant component and the second implant component are physically separated and not connected to each other. A positioning of the representation of the second implant component that violates at least one positioning constraint is prevented, wherein the positioning constraint is based on the representation of the first implant component.

A cartilage-tissue analysis device includes: an illuminating fiber that emits, from a light-emission surface thereof, laser light coming from a laser light source; two light-collecting fibers that receives scattered light at respective light-receiving surfaces the distances from which to the light-emission surface differ from each other; and detector that detects Raman spectra from the scattered light received by the light-collecting fibers. The cartilage-tissue analysis device is configure to: calculate an intensity ratio between two Raman bands originating from cartilage tissue and subchondral bone tissue, respectively, from each of the two Raman spectra; and evaluate a state of the cartilage tissue by selecting, from among the two Raman spectra, a Raman spectrum the intensity ratio of which is within a prescribed range and that analyzes the selected Raman spectrum, to analyze the selected Raman spectrum.

Methods and devices are disclosed relating improved articular models, implant components, and related guide tools and procedures. In addition, methods and devices are disclosed relating articular models, implant components, and/or related guide tools and procedures that include one or more features derived from patient-data, for example, images of the patient's joint. The data can be used to create a model for analyzing a patient's joint and to devise and evaluate a course of corrective action. The data also can be used to create patient-adapted implant components and related tools and procedures.

A method includes registering a joint of a patient in physical space with a model of the joint in virtual space and planning a planned placement of an implant to be implanted in the joint by performing a soft tissue assessment of the joint and fitting a representation of the implant relative to the model of the joint based on a result of the soft tissue assessment. The method also includes providing, based on the planned placement, a limit on movement of a surgical tool to facilitate preparation of the joint to receive the implant in the planned placement.

In some embodiments, spinal disc degeneracy is diagnosed according to a decay variance map generated by determining a variance in T2 decay over time within each pixel or pixel subset of an MRI image. A region of interest may be defined as including nucleus pulposus (NP) and annulus fibrosus (AF) areas, and excluding cartilaginous endplate (EP) areas of a disc. A decay variance for a pixel or groups of pixels is calculated by determining ratios between consecutive intensity values of the T2 signal, determining differences between consecutive ratios, and summing the absolute values of the determined differences. Decay variance mapping may be used to diagnose degeneracy in other tissues, such as in joints.

Methods and devices are disclosed relating improved articular models, implant components, and related guide tools and procedures. In addition, methods and devices are disclosed relating articular models, implant components, and/or related guide tools and procedures that include one or more features derived from patient-data, for example, images of the patient's joint. The data can be used to create a model for analyzing a patient's joint and to devise and evaluate a course of corrective action. The data also can be used to create patient-adapted implant components and related tools and procedures.

A method for intraoperative state analysis and classification of articular tissue comprising the steps of: sensing data of the articular tissue via a plurality of sensors situated in proximity to the articular tissue for detecting a response to a stimulus; determining state analysis and classification information through a verified means; processing the sensed data into a form adapted to be evaluated against a comparator; generating a means of predicting the state analysis and classification utilizing a historical dataset of processed information; and performing the state analysis and classification using the generated means based on processing the stimulus response obtained intraoperatively.

In some embodiments, spinal disc degeneracy is diagnosed according to a decay variance map generated by determining a variance in T2 decay over time within each pixel or pixel subset of an MRI image. A region of interest may be defined as including nucleus pulposus (NP) and annulus fibrosus (AF) areas, and excluding cartilaginous endplate (EP) areas of a disc. A decay variance for a pixel or groups of pixels is calculated by determining ratios between consecutive intensity values of the T2 signal, determining differences between consecutive ratios, and summing the absolute values of the determined differences. Decay variance mapping may be used to diagnose degeneracy in other tissues, such as in joints.