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PurposeInappropriate use of MRI leads to increasing interventions and surgeries for low back pain (LBP). We probed the potential effects of a routine MRI report on the patient’s perception of his spine and functional outcome of treatment. An alternate ‘clinical reporting’ was developed and tested for benefits on LBP perception.MethodsIn Phase-I, 44 LBP patients were randomized to Group A who had a factual explanation of their MRI report or Group B, who were reassured that the MRI findings showed normal changes. The outcome was compared at 6 weeks by VAS, PSEQ-2, and SF-12. In Phase-II, clinical reporting was developed, avoiding potential catastrophizing terminologies. In Phase-III, 20 MRIs were reported by both routine and clinical methods. The effects of the two methods were tested on four categories of health care professionals (HCP) who read them blinded on their assessment of severity of disease, possible treatment required, and the probability of surgery.ResultsBoth groups were comparable initial by demographics and pain. After 6 weeks of treatment, Group A had a more negative perception of their spinal condition, increased catastrophization, decreased pain improvement, and poorer functional status(p = significant for all). The alternate method of clinical reporting had significant benefits in assessment of lesser severity of the disease, shift to lesser severity of intervention and surgery in three groups of HCPs.ConclusionRoutine MRI reports produce a negative perception and poor functional outcomes in LBP. Focussed clinical reporting had significant benefits, which calls for the need for ‘clinical reporting’ rather than ‘Image reporting’.

In this work, we investigate multimodal foundation models (MFMs) for EmoFake detection (EFD) and hypothesize that they will outperform audio foundation models (AFMs). MFMs due to their cross-modal pre-training, learns emotional patterns from multiple modalities, while AFMs rely only on audio. As such, MFMs can better recognize unnatural emotional shifts and inconsistencies in manipulated audio, making them more effective at distinguishing real from fake emotional expressions. To validate our hypothesis, we conduct a comprehensive comparative analysis of state-of-the-art (SOTA) MFMs (e.g. LanguageBind) alongside AFMs (e.g. WavLM). Our experiments confirm that MFMs surpass AFMs for EFD. Beyond individual foundation models (FMs) performance, we explore FMs fusion, motivated by findings in related research areas such synthetic speech detection and speech emotion recognition. To this end, we propose SCAR, a novel framework for effective fusion. SCAR introduces a nested cross-attention mechanism, where representations from FMs interact at two stages sequentially to refine information exchange. Additionally, a self-attention refinement module further enhances feature representations by reinforcing important cross-FM cues while suppressing noise. Through SCAR with synergistic fusion of MFMs, we achieve SOTA performance, surpassing both standalone FMs and conventional fusion approaches and previous works on EFD.

In this work, we focus on non-verbal vocal sounds emotion recognition (NVER). We investigate mamba-based audio foundation models (MAFMs) for the first time for NVER and hypothesize that MAFMs will outperform attention-based audio foundation models (AAFMs) for NVER by leveraging its state-space modeling to capture intrinsic emotional structures more effectively. Unlike AAFMs, which may amplify irrelevant patterns due to their attention mechanisms, MAFMs will extract more stable and context-aware representations, enabling better differentiation of subtle non-verbal emotional cues. Our experiments with state-of-the-art (SOTA) AAFMs and MAFMs validates our hypothesis. Further, motivated from related research such as speech emotion recognition, synthetic speech detection, where fusion of foundation models (FMs) have showed improved performance, we also explore fusion of FMs for NVER. To this end, we propose, RENO, that uses renyi-divergence as a novel loss function for effective alignment of the FMs. It also makes use of self-attention for better intra-representation interaction of the FMs. With RENO, through the heterogeneous fusion of MAFMs and AAFMs, we show the topmost performance in comparison to individual FMs, its fusion and also setting SOTA in comparison to previous SOTA work.