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Surgical Phase Recognition Method with a Sequential Consistency for CAOS-AI Navigation System
روش تشخیص مرحله جراحی با یک سازگاری متوالی برای سیستم ناوبری CAOS-AI-2020 The procedure of orthopedic surgery is quite
complicated, and many kinds of equipment have been used.
Operating room nurses who deliver surgical instruments to surgeon
are supposed to be forced to incur a heavy burden. There are some
studies to recognize surgical phase with convolutional neural
network (CNN) in minimally invasive laparoscopic surgery only.
Previously, we proposed a computer-aided orthopedic surgery
(CAOS)-AI navigation system based on CNN. However, the work
propose a method to improve accuracy of phase recognition by
considering temporal dependency of orthopedic surgery video
acquired from surgeon-wearable video camera. The method
estimates current surgical phase by combining both temporal
dependency and convolutional-long-short term memory network
(CNN-LSTM). Experimental results shows a phase recognition
accuracy of 59.9% by the proposed method applied in unicomapartmenatal
knee arthroplasty (UKA). Keywords: Deep Learning | Computer-aided Orthopaedic Surgery | Operating Room Nurse | Phase Recognition |
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