Intelligence" refers to a computational system, module, of the like capable of performing tasks typically requiring human intelligence. These tasks include learning from examples, pattern recognition, decision-making, natural language understanding, and more. AI systems and/or modules can employ a variety of models and techniques, including artificial neural networks (ANNs), machine learning, and deep learning. … more Intelligence" refers to a computational system, module, of the like capable of performing tasks typically requiring human intelligence. These tasks include learning from examples, pattern recognition, decision-making, natural language understanding, and more. AI systems and/or modules can employ a variety of models and techniques, including artificial neural networks (ANNs), machine learning, and deep learning.

"Module" refers to a functionally distinct component or logical unit within a system, apparatus, method, or software solution that is configured to perform one or more specific operations. Synonyms for “module” may include component, subsystem, unit, segment, engine, block, or the like. A module may be implemented in hardware, software, firmware, or any combination thereof. A module may be embodied as a software routine, class, object, process, or service, or as a hardware circuit, integrated chip, programmable logic component, or the like. A module may operate independently or in cooperation with one or more other modules and may be configured to receive, process, generate, transmit, or store data, or the like. A module may be distributed across computing environments or reside within a single computational entity. … more "Module" refers to a functionally distinct component or logical unit within a system, apparatus, method, or software solution that is configured to perform one or more specific operations. Synonyms for “module” may include component, subsystem, unit, segment, engine, block, or the like. A module may be implemented in hardware, software, firmware, or any combination thereof. A module may be embodied as a software routine, class, object, process, or service, or as a hardware circuit, integrated chip, programmable logic component, or the like. A module may operate independently or in cooperation with one or more other modules and may be configured to receive, process, generate, transmit, or store data, or the like. A module may be distributed across computing environments or reside within a single computational entity.

“Generative Network” refers to a model configured to synthesize data samples (e.g., images) from random inputs and/or conditioning signals. Examples include GANs, variational autoencoders (VAEs), diffusion models, autoregressive models, or hybrids. A generative network may be conditioned on pixel-wise representations, masks/label maps, edges, landmarks, text prompts, or other anatomic data. … more “Generative Network” refers to a model configured to synthesize data samples (e.g., images) from random inputs and/or conditioning signals. Examples include GANs, variational autoencoders (VAEs), diffusion models, autoregressive models, or hybrids. A generative network may be conditioned on pixel-wise representations, masks/label maps, edges, landmarks, text prompts, or other anatomic data.

“Convolutional Neural Network (CNN)” refers to a neural network employing convolutional operations (optionally with pooling and up/down-sampling) suited to images or grid-structured data. Examples include U-Net and U-Net-derived encoder–decoder architectures with skip connections. CNNs may be used for segmentation, detection, classification, denoising, super-resolution, and the like. … more “Convolutional Neural Network (CNN)” refers to a neural network employing convolutional operations (optionally with pooling and up/down-sampling) suited to images or grid-structured data. Examples include U-Net and U-Net-derived encoder–decoder architectures with skip connections. CNNs may be used for segmentation, detection, classification, denoising, super-resolution, and the like.

“Deep Learning (DL)” refers to the use of neural networks with multiple layers (deep neural networks) that learn hierarchical feature representations. Deep learning architectures include, without limitation, convolutional neural networks (CNNs), recurrent networks, and transformers, and may be trained in supervised or unsupervised/self-supervised regimes. … more “Deep Learning (DL)” refers to the use of neural networks with multiple layers (deep neural networks) that learn hierarchical feature representations. Deep learning architectures include, without limitation, convolutional neural networks (CNNs), recurrent networks, and transformers, and may be trained in supervised or unsupervised/self-supervised regimes.

“Machine Learning (ML)” refers to techniques by which models improve performance from data. ML may be supervised, semi-supervised, self-supervised, unsupervised, or reinforcement learning, and may address tasks such as classification, regression, clustering, dimensionality reduction, and segmentation. Representative models include, without limitation, neural networks, transformers, support-vector machines, decision trees/ensembles, Bayesian models, and clustering algorithms. Models may be trained by minimizing a loss function and evaluated using metrics such as accuracy, precision/recall/F1, ROC/AUC, or the like. … more “Machine Learning (ML)” refers to techniques by which models improve performance from data. ML may be supervised, semi-supervised, self-supervised, unsupervised, or reinforcement learning, and may address tasks such as classification, regression, clustering, dimensionality reduction, and segmentation. Representative models include, without limitation, neural networks, transformers, support-vector machines, decision trees/ensembles, Bayesian models, and clustering algorithms. Models may be trained by minimizing a loss function and evaluated using metrics such as accuracy, precision/recall/F1, ROC/AUC, or the like.

“Artificial Neural Network (ANN)” refers to a parameterized computational architecture comprising layers of interconnected units (neurons) that apply learned weights and nonlinear activation functions to input data. ANNs may be trained by backpropagation (optionally with stochastic gradient methods) and may include components such as convolutional layers, pooling, normalization, attention, and skip connections. ANN families include feedforward, convolutional, recurrent, and transformer-based networks. … more “Artificial Neural Network (ANN)” refers to a parameterized computational architecture comprising layers of interconnected units (neurons) that apply learned weights and nonlinear activation functions to input data. ANNs may be trained by backpropagation (optionally with stochastic gradient methods) and may include components such as convolutional layers, pooling, normalization, attention, and skip connections. ANN families include feedforward, convolutional, recurrent, and transformer-based networks.

“Anatomy Plane” refers to a geometric plane in three-dimensional space associated with an anatomical region of interest. Examples include, without limitation, a plane approximating the level of selected anatomical structures (e.g., metatarsal heads), a plantar reference plane, a sagittal or coronal plane, or the like. An anatomy plane may be defined explicitly from landmarks, fitted to points or contours, or inferred from acquisition geometry including the positions and/or orientations of imaging emitters and sensors (e.g., X-ray source and detector, fluoroscope C-arm, camera or mobile-device camera), and may be modeled as coincident with, parallel to, or at a known or estimated offset from a detector plane or display plane. An anatomy plane may also be estimated computationally. As used herein, “at an anatomy plane” denotes that a measurement or calibration corresponds to the physical scale at that anatomical level; where the anatomy is not perfectly planar, the term includes a representative plane within an acceptable tolerance. In one embodiment, the anatomy plane is used for patient-plane calibration to compute a pixel-to-length conversion in millimeters per pixel. … more “Anatomy Plane” refers to a geometric plane in three-dimensional space associated with an anatomical region of interest. Examples include, without limitation, a plane approximating the level of selected anatomical structures (e.g., metatarsal heads), a plantar reference plane, a sagittal or coronal plane, or the like. An anatomy plane may be defined explicitly from landmarks, fitted to points or contours, or inferred from acquisition geometry including the positions and/or orientations of imaging emitters and sensors (e.g., X-ray source and detector, fluoroscope C-arm, camera or mobile-device camera), and may be modeled as coincident with, parallel to, or at a known or estimated offset from a detector plane or display plane. An anatomy plane may also be estimated computationally. As used herein, “at an anatomy plane” denotes that a measurement or calibration corresponds to the physical scale at that anatomical level; where the anatomy is not perfectly planar, the term includes a representative plane within an acceptable tolerance. In one embodiment, the anatomy plane is used for patient-plane calibration to compute a pixel-to-length conversion in millimeters per pixel.

As used herein, "Plumbline (PL)" refers to a radiographic assessment reference construct and/or anatomic data representing forefoot alignment, used to evaluate whether space exists to align the first ray relative to a longitudinal foot axis without requiring lesser-ray realignment. In one embodiment, a PL is a constructed reference. In an AP projection or view (including dorsoplantar (DP) equivalents), the PL is constructed from landmarks associated with the medial cuneiform and the first tarsometatarsal (TMT) joint and extended distally toward the lesser metatarsals. A ‘positive PL’ indicates that the PL intersects the second metatarsal head (or shaft), suggesting metatarsus adductus (MTA) of a degree that may impede complete first-ray correction; a ‘negative PL’ indicates that the PL is tangential to, or does not intersect, the second metatarsal, suggesting sufficient clearance for first-ray correction. Minor variations in beam angle, source-to-detector distance, patient positioning, camera viewpoint, or reconstruction that do not materially alter the assessment are included. In some embodiments, a proximity tolerance ε is used to classify borderline cases as intersecting or tangential. McAleer, J. P., DeCarbo, W. T., D’Antonio, P. C., & Hatch, D. J. (2024). "A Simplified Preoperative Radiographic Assessment for Metatarsus Adductus Associated With Hallux Valgus." Foot & Ankle Orthopaedics, 9(2). https://doi.org/10.1177/24730114241246830[] … more As used herein, "Plumbline (PL)" refers to a radiographic assessment reference construct and/or anatomic data representing forefoot alignment, used to evaluate whether space exists to align the first ray relative to a longitudinal foot axis without requiring lesser-ray realignment. In one embodiment, a PL is a constructed reference. In an AP projection or view (including dorsoplantar (DP) equivalents), the PL is constructed from landmarks associated with the medial cuneiform and the first tarsometatarsal (TMT) joint and extended distally toward the lesser metatarsals. A ‘positive PL’ indicates that the PL intersects the second metatarsal head (or shaft), suggesting metatarsus adductus (MTA) of a degree that may impede complete first-ray correction; a ‘negative PL’ indicates that the PL is tangential to, or does not intersect, the second metatarsal, suggesting sufficient clearance for first-ray correction. Minor variations in beam angle, source-to-detector distance, patient positioning, camera viewpoint, or reconstruction that do not materially alter the assessment are included. In some embodiments, a proximity tolerance ε is used to classify borderline cases as intersecting or tangential. McAleer, J. P., DeCarbo, W. T., D’Antonio, P. C., & Hatch, D. J. (2024). "A Simplified Preoperative Radiographic Assessment for Metatarsus Adductus Associated With Hallux Valgus." Foot & Ankle Orthopaedics, 9(2). https://doi.org/10.1177/24730114241246830[]

"Constructed Reference” refers to a derived geometric construct computed from landmarks or anatomic data, including points, centroids, axes, lines, rays, curves, planes, regions, tolerance bands, or the like, used for measurement, registration, planning, guidance, or assessment. Constructed references may be rendered as annotations and/or stored as data. … more "Constructed Reference” refers to a derived geometric construct computed from landmarks or anatomic data, including points, centroids, axes, lines, rays, curves, planes, regions, tolerance bands, or the like, used for measurement, registration, planning, guidance, or assessment. Constructed references may be rendered as annotations and/or stored as data.

“Geometric Rectification” refers to processing that transforms a captured image to compensate for perspective and/or lens distortion and to isolate a depiction of an image of interest, including, without limitation, homography or projective warping, undistortion, dewarping, rotation, scaling, cropping, resampling, glare/reflection suppression, and the like. … more “Geometric Rectification” refers to processing that transforms a captured image to compensate for perspective and/or lens distortion and to isolate a depiction of an image of interest, including, without limitation, homography or projective warping, undistortion, dewarping, rotation, scaling, cropping, resampling, glare/reflection suppression, and the like.

“Hallux Valgus Angle (HVA)” refers to an angular measurement formed between a long axis of the first metatarsal and a long axis of the proximal phalanx of the hallux, typically measured on an anterior–posterior view. HVA may be computed from anatomic data and/or constructed references and may be used to classify deformity severity. … more “Hallux Valgus Angle (HVA)” refers to an angular measurement formed between a long axis of the first metatarsal and a long axis of the proximal phalanx of the hallux, typically measured on an anterior–posterior view. HVA may be computed from anatomic data and/or constructed references and may be used to classify deformity severity.

“Intermetatarsal Angle (IMA)” refers to an angular measurement formed between long axes of the first and second metatarsals, typically measured on an anterior–posterior view. IMA may be computed from anatomic data and/or constructed references and may be used to select and/or evaluate correction procedures. … more “Intermetatarsal Angle (IMA)” refers to an angular measurement formed between long axes of the first and second metatarsals, typically measured on an anterior–posterior view. IMA may be computed from anatomic data and/or constructed references and may be used to select and/or evaluate correction procedures.

“Lesser-Ray Realignment” refers to any correction of alignment of one or more lesser rays (metatarsals two through five), including, without limitation, osteotomies, arthrodesis, soft-tissue procedures, image-guided manipulations, or the like, performed alone or in combination with other corrections. … more “Lesser-Ray Realignment” refers to any correction of alignment of one or more lesser rays (metatarsals two through five), including, without limitation, osteotomies, arthrodesis, soft-tissue procedures, image-guided manipulations, or the like, performed alone or in combination with other corrections.

“Masks” refers to pixel-wise representations of regions or classes in an image, including, without limitation, binary masks, label maps, instance masks, probability maps, heatmaps, run-length–encoded masks, vector contours rasterized to a grid, or the like, at native or resampled resolution. … more “Masks” refers to pixel-wise representations of regions or classes in an image, including, without limitation, binary masks, label maps, instance masks, probability maps, heatmaps, run-length–encoded masks, vector contours rasterized to a grid, or the like, at native or resampled resolution.

“Metatarsus Adductus (MTA)” refers to medial deviation of the forefoot relative to the midfoot and/or hindfoot in the transverse plane, characterized by adduction of one or more metatarsals toward the foot midline. MTA may be flexible or rigid and may be assessed using angular, axis-based, or categorical measures. … more “Metatarsus Adductus (MTA)” refers to medial deviation of the forefoot relative to the midfoot and/or hindfoot in the transverse plane, characterized by adduction of one or more metatarsals toward the foot midline. MTA may be flexible or rigid and may be assessed using angular, axis-based, or categorical measures.

“Metatarsus Adductus Metric” refers to any quantitative or categorical representation of MTA, including, without limitation, angles, distances, ratios, scores, thresholds, or a categorical indicator derived from one or more constructed references, and the like. … more “Metatarsus Adductus Metric” refers to any quantitative or categorical representation of MTA, including, without limitation, angles, distances, ratios, scores, thresholds, or a categorical indicator derived from one or more constructed references, and the like.

“Patient-Plane Calibration” refers to determining a scale factor that converts pixel measurements to physical dimensions at the anatomical plane of interest. In one embodiment, calibration is obtained using a calibration object of known size positioned at the anatomy plane; in another embodiment, calibration is computed from imaging geometry and/or metadata. … more “Patient-Plane Calibration” refers to determining a scale factor that converts pixel measurements to physical dimensions at the anatomical plane of interest. In one embodiment, calibration is obtained using a calibration object of known size positioned at the anatomy plane; in another embodiment, calibration is computed from imaging geometry and/or metadata.

“Pixel-to-Length Conversion” refers to a mapping from pixel units to physical units (e.g., millimeters per pixel) applicable to an image or region thereof. The conversion may be a scalar, vector, or spatially varying function and may be derived from patient-plane calibration, metadata, or geometric correction. … more “Pixel-to-Length Conversion” refers to a mapping from pixel units to physical units (e.g., millimeters per pixel) applicable to an image or region thereof. The conversion may be a scalar, vector, or spatially varying function and may be derived from patient-plane calibration, metadata, or geometric correction.

“Pixel-Wise Representations” refers to data in which values are defined per pixel (or voxel), including, without limitation, masks, label maps, probability maps, heatmaps, distance transforms, or the like, used to represent anatomy, landmarks, or other features. … more “Pixel-Wise Representations” refers to data in which values are defined per pixel (or voxel), including, without limitation, masks, label maps, probability maps, heatmaps, distance transforms, or the like, used to represent anatomy, landmarks, or other features.