Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists.

BACKGROUND: Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists. OBJECTIVE: The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand. MATERIALS AND METHODS: We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages. RESULTS: The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months, P=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months, P<0.0001). CONCLUSION: CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.

Experimental platform for the study of region specific excitation and inhibition in neural tissue.

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this is due to the fact that experimental validation of the evoked response to stimuli is an arduous and time-consuming task. The development of a high-throughput data acquisition and analysis tool would greatly facilitate the design of spatially selective stimulation protocols. We present an automated imaging system that can optically track and identify the action potentials of individual neurons evoked by coordinated stimulus waveforms applied at multiple electrodes. This system can simultaneously provide arbitrary current waveforms to four electrodes, and it is capable of automatically monitoring the cellular responses of every neuron in a cultured network within a 1.6 x 1.6 mm area. The purpose of this platform is to develop stimulus protocols that exploit the benefits of multi-polar field shaping and temporal ion-channel manipulation to localize cellular excitation beyond the vicinity of the electrode. Preliminary single electrode experiments demonstrate that spatially selective stimulus suppression may be achieved with cathodic, depolarizing prepulses that induce a sub-threshold refractory state in neighboring neurons. Coordinated, multi-site stimuli could potentially take advantage of this refractory state to direct the stimulus focus away from the surrounding area of the electrode and into the inter-electrode spaces.