Encoding and immediate retrieval tasks in patients with epilepsy: A functional MRI study of verbal and visual memory.

PURPOSE: Medial lobe temporal structures and more specifically the hippocampus play a decisive role in episodic memory. Most of the memory functional magnetic resonance imaging (fMRI) studies evaluate the encoding phase; the retrieval phase being performed outside the MRI. We aimed to determine the ability to reveal greater hippocampal fMRI activations during retrieval phase. MATERIALS AND METHODS: Thirty-five epileptic patients underwent a two-step memory fMRI. During encoding phase, subjects were requested to identify the feminine or masculine gender of faces and words presented, in order to encourage stimulus encoding. One hour after, during retrieval phase, subjects had to recognize the word and face. We used an event-related design to identify hippocampal activations. RESULTS: There was no significant difference between patients with left temporal lobe epilepsy, patients with right temporal lobe epilepsy and patients with extratemporal lobe epilepsy on verbal and visual learning task. For words, patients demonstrated significantly more bilateral hippocampal activation for retrieval task than encoding task and when the tasks were associated than during encoding alone. Significant difference was seen between face-encoding alone and face retrieval alone. CONCLUSIONS: This study demonstrates the essential contribution of the retrieval task during a fMRI memory task but the number of patients with hippocampal activations was greater when the two tasks were taken into account.

Radiographic Detection of Post-Traumatic Bone Fractures: Contribution of Artificial Intelligence Software to the Analysis of Senior and Junior Radiologists.

Objectives: The aims of this study were: (a) to evaluate the performance of an artificial intelligence (AI) software package (Boneview Trauma, Gleamer) for the detection of post-traumatic bone fractures in radiography as a standalone; (b) used by two radiologists (osteoarticular senior and junior); and (c) to determine to whom AI would be most helpful. Materials and Methods: Within 14 days of a trauma, 101 consecutive patients underwent radiographic examination of the upper or lower limbs. The definite diagnosis for identifying fractures was: (a) radio-clinical consensus between the radiologist on-call who analyzed the images and the orthopedist (Group 1); (b) Cone Beam computed tomography (CBCT) exploration of the area of interest, in case of doubts or absence of consensus (Group 2). Independently of this diagnosis for both groups, the radiographic images were separately analyzed by two radiologists (osteoarticular senior: SR; junior: JR) prior without, and thereafter with the results of AI. Results: AI performed better than the radiologists in detecting common fractures (Group 1), but not subtle fractures (Group 2). In association with AI, both radiologists increased their overall performances in both groups, whereas this increase was significantly higher for the JR (p < 0.05). Conclusion: AI is reliable for common radiographic fracture identification and is a useful learning tool for radiologists in training. However, the software's overall performance does not exceed that of an osteoarticular senior radiologist, particularly in case of subtle lesions.