PT  - JOURNAL ARTICLE
AU  - Rafik Margaryan
AU  - Daniele Della Latta
AU  - Giacomo Bianchi
AU  - Nicola Martini
AU  - Gianmarco Santini
AU  - Dante Chiappino
AU  - Marco Solinas
TI  - Intercostal Space Prediction Using Deep Learning In Fully Endoscopic Mitral Valve Surgery
AID  - 10.1101/710996
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 710996
4099  - http://biorxiv.org/content/early/2019/07/22/710996.short
4100  - http://biorxiv.org/content/early/2019/07/22/710996.full
AB  - Objective About 10 million people in Europe suffer from mitral valve incompetence. Majority of these entity is mitral valve prolapse in developed countries. Endoscopic mitral valve surgery is a relatively new procedure and preparation in the right intercostal space are crucial for success completion of the procedure. We aimed to explore clinical variables and chest X-rays in order to build most performant model that can predict the right intercostal space for thoracotomy.Methods Overall 234 patients underwent fully endoscopic mitral valve surgery. All patients had preoperative two projection radiography. Intercostal space for right thoracotomy was decided by expert cardiac surgeons taking in consideration the height, weight, chest radiography, anatomical position of skin incision, nipple position and the sex. In order to predict the right intercostal space we have used clinical data and we have collected all radiographies and feed it to deep neural network algorithm. We have spitted the whole data-set into two subsets: training and testing data-sets. We have used clinical data and build an algorithm (Random Forest) in order to have reference model.Results The best-performing classifier was GoogLeNet neural network (now on we will reffera as Deep Learning) and had an AUC of 0.956. Algorithm based on clinical data (Random Forest) had AUC of 0.529 using only chest x-rays. The deep leaning algorithm predicted correctly in all cases the correct intercostal space on the training datasest except two ladies (96.08% ; with sensitivity of 97.06% and specificity 94.12 %, where the Random Forest was capable to predict right intercostal space in 60.78% cases with sensitivity of 93.33% and specificity 14.29 % (only clinical data).Conclusion Artificial intelligence can be helpful to program the minimally invasive cardiac operation, for right intercostal space selection for thoracotomy, especially in non optimal thoraxes (example, obese short ladies). It learned from the standard imaging (thorax x-ray) which is easy, do routinely to every patient.