Diagnosis Tool
Disclaimer
The diagnosis tool is currently under heavy development. The results of the tool MUST NOT be used as a clinical diagnosis. We will push periodic updates to improve the tool and add new functionalities. Bugs are expected, if you find any please write a mail to [email protected] indicating the issue. Feedback is greatly appreciated, if you have any suggestions or comments please feel free to share them with us.
The tool is provided "as is", without warranty of any kind, express or implied. In no event shall the authors or copyright holders be liable for any claim, damages or other liability arising from, out of or in connection with the tool or the use or other dealings in the tool.
What is the Diagnosis Tool?
The diagnosis tool allows using the diagnosis Machine Learning model to obtain the probability of multiple diagnoses. It offers an interactive user interface to score the degree of fat infiltration in the muscles and allows to export or import the data for future use.
Getting Started
You can access the diagnosis tool here. You will be presented with the following layout:
- Regions Panel
- Toolbar
- Mercuri Table
The Mercuri table shows the current Mercuri scores for the muscles, distinguishing between the right and left sides. Note that the right side is represented in the left column, and the left side is represented in the right column. This is done to better represent the MRI slices, as due to perspective the right leg appears as the left one to the viewer and vice versa. The Mercuri score can be empty (blank) or have a value between 0 and 4.
In the regions panel you can find representations of some MRI axial slices. The panel is divided into three regions, that you can navigate using the different tabs: Pelvis, Thigh and Calf. There are multiple slices available for each region, that you can navigate using the numbered buttons. Muscles don't have a score by default and appear unfilled, you can interact with the muscles to increase or decrease their Mercuri score. To interact with a muscle, hover it with the pointer and perform one of the following actions: click or scroll. If you click, an input box will open at the top-right of the regions panel indicating the name of the muscle. You can modify the Mercuri score of the muscle by introducing it in the input box. If you scroll, the Mercuri score of the hovered muscle will increase (if scrolling up) or decrease (if scrolling down). Some muscles use a shorter name in the number input, you can display the full name by hovering the muscle and leaving the mouse still.
When the Mercuri score of a muscle is modified, the colour of the muscle will change to represent the new score. The new value will be also updated in the Mercuri table. If the colour scale used to represent the Mercuri scores is not clear, you can change it using the "Colormap" dropdown in the toolbar.
The toolbar has also buttons for downloading or uploading the Mercuri scores in CSV format. Uploaded CSV files must follow the same structure and format that the downloaded ones. You can get a tooltip about each button by hovering it.
Once all muscles have been scored, click the "Submit" button over the Mercuri table. A loading screen will show while the machine learning model is predicting the diagnostic probabilities. Once this is done, you will be redirected to the results page.
Mercuri Score
The Mercuri Score is a semi-quantitative scale to measure the fat replacement in muscles. To learn more about the Mercuri score, whatch the video below.
Data Imputation
Missing data is a phenomenon that disables most Machine Learning techniques, including the ones used by MYO-Guide. Ideally, the MRIs should cover all the leg volume, from the Psoas to the Flexor Hallucis/Digitorum Longus muscles. However, missing data is unavoidable when the MRI does not cover the full volume of the leg or any image artefact making scoring a muscle impossible. For these cases, MYO-Guide implements a data imputation mechanism that attempts to approximate the missing values.
While this mechanism introduces some bias, it is useful when some muscles are missing. However, if a whole region is missing or there is a large proportion of missing data, the results of the tool should be considered carefully.
Supported Diagnostics
The diagnosis tool currently supports the following diagnoses:
| Gene | Disease | Other Names |
|---|---|---|
| ANO5 | Anoctaminopathy | LGMD-R12, ANO5 muscle disease, LGMD2L |
| CAPN3 | Calpainopathy | LMGD-R1, LGMD-D4, LGMD1I, LGMD2A |
| DMD | Dystrophynopathy | DMD, BMD, Duchenne / Becker muscular dystrophy |
| FKRP | FKRP related LGMD / Congenital muscle dystrophy | LGMD-R9, LGMD2I, MDC1C |
| DMPK | Myotonic dystrophy type I | MD1, DM1, Steinert disease, dystrophia myotonica type 1 |
| SMN-1 | Spinal muscular atrophy | SMA, Werdnig Hoffmann disease, Kugelberg Welander disease |
| GNE | GNE myopathy | Nonaka disease, hereditary inclusion body miositis (HIBM), IBM2, distal myopathy with rimmed vacuoles |
| GAA | Pompe Disease | Glycogen storage disease type II, acid alpha-glucosidase deficiency, acid maltase deficiency |
| LMNA | Laminin alpha 2 muscular dystrophy | LGMD1B |
| PABPN1 | Oculopharyngeal muscle dystrophy | PABP2, OPMD |
| DYSF | Dysferlinopathy | LGMD-R2, Miyoshi myopathy, distal anterior compartment myopathy, LGMD2B |
| DUX4 | Facio-Scapulo-Humeral muscular dystrophy type 1 | D4Z4, FSHD1 |
| SarcoG | Alfa/Beta/Gamma sarcoglycanopathy | LGMD-R3, LGMD2D / LGMD-R4, LGMD2E / LGMD-R5, LGMD2C |
| TTN | Titinopathy | LGMD-R10, LGMD2J |
| VCP | Vallosin containing protein (VCP) myopathy | Inclusion body myopathy associated with Paget's disease of bone and frontotemporal dementia (IBMPFD) |