Kento Morita, Shigehiro Karashima, Toshiki Terao, Kotaro Yoshida, Takeshi Yamashita, Takeshi Yoroidaka, Mikoto Tanabe, Tatsuya Imi, Yoshitaka Zaimoku, Akiyo Yoshida, Hiroyuki Maruyama, Noriko Iwaki, Go Aoki, Takeharu Kotani, Ryoichi Murata, Toshihiro Miyamoto, Youichi Machida, Kosei Matsue, Hidetaka Nambo, Hiroyuki Takamatsu
Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers...
March 8, 2024: Journal of Medical Systems