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Papers on AI tools applied to diagnoses of knee conditions.

Journal: Medicina

Article Title: Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review

doi: 10.3390/medicina61040737

Figure Lengend Snippet: Papers on AI tools applied to diagnoses of knee conditions.

Article Snippet: Hoffmann et al. [ ] tested the performance of a CNN implemented in a common planning software (mediCAD ® 7.0; mediCAD Hectec GmbH) that allows analysis and preoperative planning.

Techniques: Imaging, Diagnostic Assay, Sequencing, Software, Labeling, Biomarker Discovery, Magnetic Resonance Imaging

Papers reviewed on AI tools applied to surgical planning.

Journal: Medicina

Article Title: Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review

doi: 10.3390/medicina61040737

Figure Lengend Snippet: Papers reviewed on AI tools applied to surgical planning.

Article Snippet: Hoffmann et al. [ ] tested the performance of a CNN implemented in a common planning software (mediCAD ® 7.0; mediCAD Hectec GmbH) that allows analysis and preoperative planning.

Techniques: Imaging, Diagnostic Assay, Software, Immunocytochemistry, Generated, Functional Assay, Biomarker Discovery

Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

Journal: BJA Open

Article Title: Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study ☆

doi: 10.1016/j.bjao.2023.100236

Figure Lengend Snippet: Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

Article Snippet: A CNN in RStudio was built based on a published example of code used to process the Modified National Institute of Standards and Technology (MNIST) image dataset of handwritten digits ( ).

Techniques: Modification, Muscles, Activation Assay