Artificial intelligence in musculoskeletal oncological radiology

Due to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplasms

Identification of motor unit firings in H-reflex of soleus muscle recorded by high-density surface electromyography

We developed and tested the methodology that supports the identification of individual motor unit (MU) firings from the Hoffman (or H) reflex recorded by surface high-density EMG (HD-EMG). Synthetic HD-EMG signals were constructed from simulated 10% to 90% of maximum voluntary contraction - MVC, followed by 100 simulated H-reflexes. In each H-reflex the MU firings were normally distributed with mean latency of 20 ms and standard deviations (SDLAT) ranging from 0.1 to 1.3 ms. Experimental H-reflexes were recorded from the soleus muscle of 12 men (33.6 ± 5.8 years) using HD-EMG array of 5×13 surface electrodes. Participants performed 15 to 20 s long voluntary plantarflexions with contraction levels ranging from 10% to 70% MVC. Afterwards, at least 60 H-reflexes were electrically elicited at three levels of background muscle activity: rest, 10% and 20% MVC. HD-EMGs of voluntary contractions were decomposed using the Convolution Kernel Compensation method to estimate the MU filters. When applied to HD-EMG signals with synthetic H reflexes, MU filters demonstrated high MU identification accuracy, especially for SDLAT > 0.3 ms. When applied to experimental H-reflex recordings, the MU filters identified 14.1 ± 12.1, 18.2 ± 12.1 and 20.8 ± 8.7 firings per H-reflex, with individual MU firing latencies of 35.9 ± 3.3, 35.1 ± 3.0 and 34.6 ± 3.3 ms for rest, 10% and 20% MVC background muscle activity, respectively. Standard deviation of MU latencies across experimental H-reflexes were 1.0 ± 0.8, 1.3 ± 1.1 and 1.5 ± 1.2 ms, in agreement with intramuscular EMG studies.</p

Identification of motor unit firings in H-reflex of soleus muscle recorded by high-density surface electromyography

We developed and tested the methodology that supports the identification of individual motor unit (MU) firings from the Hoffman (or H) reflex recorded by surface high-density EMG (HD-EMG). Synthetic HD-EMG signals were constructed from simulated 10% to 90% of maximum voluntary contraction - MVC, followed by 100 simulated H-reflexes. In each H-reflex the MU firings were normally distributed with mean latency of 20 ms and standard deviations (SDLAT) ranging from 0.1 to 1.3 ms. Experimental H-reflexes were recorded from the soleus muscle of 12 men (33.6 ± 5.8 years) using HD-EMG array of 5×13 surface electrodes. Participants performed 15 to 20 s long voluntary plantarflexions with contraction levels ranging from 10% to 70% MVC. Afterwards, at least 60 H-reflexes were electrically elicited at three levels of background muscle activity: rest, 10% and 20% MVC. HD-EMGs of voluntary contractions were decomposed using the Convolution Kernel Compensation method to estimate the MU filters. When applied to HD-EMG signals with synthetic H reflexes, MU filters demonstrated high MU identification accuracy, especially for SDLAT > 0.3 ms. When applied to experimental H-reflex recordings, the MU filters identified 14.1 ± 12.1, 18.2 ± 12.1 and 20.8 ± 8.7 firings per H-reflex, with individual MU firing latencies of 35.9 ± 3.3, 35.1 ± 3.0 and 34.6 ± 3.3 ms for rest, 10% and 20% MVC background muscle activity, respectively. Standard deviation of MU latencies across experimental H-reflexes were 1.0 ± 0.8, 1.3 ± 1.1 and 1.5 ± 1.2 ms, in agreement with intramuscular EMG studies.</p

Motor unit identification in the M waves recorded by high-density electromyogram

Objective: We describe and test the methodology supporting the identification of individual motor unit (MU) firings in the motor response (M wave) to percutaneous nerve stimulation recorded by surface high-density electromyography (HD-EMG) on synthetic and experimental data. Methods: A set of simulated voluntary contractions followed by 100 simulated M waves with a normal distribution (MU mean firing latency: 10 ms, Standard Deviation - SDLAT 0.1-1.3 ms) constituted the synthetic signals. In experimental condition, at least 52 progressively increasing M waves were elicited in the soleus muscle of 12 males, at rest (REST), and at 10% (C10) and 20% (C20) of maximal voluntary contraction (MVC). The MU decomposition filters were identified from 15-20 s long isometric plantar flexions performed at 10-70% of MVC and, afterwards, applied to M waves. Results: Synthetic signal analysis demonstrated high accuracy of MU identification in M waves (precision ≥ 85%). In experimental conditions 42.6 ± 11.2 MUs per participant were identified from voluntary contractions. When the MU filters were applied to the M wave recordings, 28.4 ± 14.3, 23.7 ± 14.9 and 20.2 ± 13.5 MU firings were identified in the maximal M waves, with individual MU firing latencies of 10.0 ± 2.8 ( SDLAT : 1.2 ± 1.2), 9.6 ± 3.0 ( SDLAT : 1.5 ± 1.3) and 10.1 ± 3.7 ( SDLAT : 1.7 ± 1.6) ms in REST, C10 and C20 conditions, respectively. Conclusion and significance: We present evidence that supports the feasibility of identifying MU firings in M waves recorded by HD-EMG.</p

Myoblast proliferation rate.

<p>The figure shows the proliferation of myoblasts after 7 days of incubation with DMEM supplemented with decorin 10 ng/ml, decorin 25 ng/ml, 10% PRP exudate and 20% PRP exudate. Three independent tests were performed and the results are expressed by the absolute absorbance values (in blue) and mean ratios (%, ± SD) of absorbance in treated wells to those in control wells (in grey). ANOVA, *p<0.001 compared with control.</p

TGF-β and MSTN expression (ELISA).

<p>Three independent tests were performed for each cytokine and the results are expressed by the mean ratios (%) of absorbance in treated wells to those in control wells. ANOVA, p<0.001 compared to control.</p

Schematic representation of muscle regeneration on the regulatory level.

<p>During skeletal muscle regeneration various MRFs are being expressed (in blue). Satellite cells differentiate into myoblasts which proliferate and either further differentiate into polynucleated myotubules or transform into myofibroblasts. TGF-β and MSTN play an important role in inhibiting/stimulating these steps (marked with +/- symbols).</p

TGF-β and myostatin expression.

<p>(a) Cultured myoblasts incubated with DMEM and supplemented with decorin 10 ng/ml, decorin 25 ng/ml, 10% PRP exudate and 20% PRP exudate (ELISA). Cytokine expression was measured after 48 hours of incubation. (b): TGF-β expression. (c): MSTN expression. Three independent tests were performed and the results were expressed by the absolute absorbance values (in grey) and mean ratios (%, ± SD) of absorbance in treated wells to those in control wells (in blue). ANOVA, p<0.001 compared with control, *p<0.005 compared with decorin. In PRP group, no cells were growing in the well in order to determine the TGF-β content in PRP.</p