Aitor Landaluce

Early the protocol If you can add to the surgery, send me a MD @StcuaeC @aecirujanos @AECP_FAECP @GermFTsurgery @germ_red @JTraumAcuteSurg @estesonline @Asiritrauma1 @RNCsantander @SWexner @ProfW_edinsurg @EstravizBegona @Belindadesimone @InesRPsurg @Curro_Blanco @CelestinoGutirr

Early the protocol If you can add to the surgery, send me a MD @StcuaeC @aecirujanos @AECP_FAECP @GermFTsurgery @germ_red @JTraumAcuteSurg @estesonline @Asiritrauma1 @RNCsantander @SWexner @ProfW_edinsurg @EstravizBegona @Belindadesimone @InesRPsurg @Curro_Blanco @CelestinoGutirr

This our New proyect in @GermFTsurgery @JoMaRaRo @LuisSanchezGlln about the influence of ERAS measures in cáncer colon obstruction EUROPEAN COLONOBSTRUCTION SURVEY(EUCOBS).ESTUDIO EUROPEO SOBRE OBSTRUCCION COLON. We are recruiting centers @StcuaeC @estesonline @coloproctoa

This our New proyect in @GermFTsurgery @JoMaRaRo @LuisSanchezGlln about the influence of ERAS measures in cáncer colon obstruction EUROPEAN COLONOBSTRUCTION SURVEY(EUCOBS).ESTUDIO EUROPEO SOBRE OBSTRUCCION COLON. We are recruiting centers @StcuaeC @estesonline @coloproctoa

Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging - Annals of Surgical Oncology Main Objectives We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal cancer using endorectal ultrasound imaging. Methods This study included 304 patients with rectal cancer from Fujian Medical University Union Hospital. The patients were randomly divided into a training group (213 patients) and a test group (91 patients) at a 7:3 ratio. Radiomics and deep learning models were established using primary tumor and peritumoral images. In the optimally performing regions-of-interest, two fusion strategies, a feature-based and a decision-based model, were employed to build the fusion models. The Shapley additive explanation (SHAP) method was used to evaluate the significance of features in the optimal radiomics, deep learning, and fusion models. The performance of each model was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results In the test cohort, both the radiomics and deep learning models exhibited optimal performance with a 10-pixel patch extension, yielding AUC values of 0.824 and 0.856, respectively. The feature-based DLRexpand10_FB model attained the highest AUC (0.896) across all study sets. In addition, the DLRexpand10_FB model demonstrated excellent sensitivity, specificity, and DCA. SHAP analysis underscored the deep learning feature (DL_1) as the most significant factor in the hybrid model. Conclusion The feature-based fusion model DLRexpand10_FB can be employed to predict KRAS gene mutations based on pretreatment endorectal ultrasound images of rectal cancer. The integration of peritumoral regions enhanced the predictive performance of both the radiomics and deep learning models.

Fusion model predicts KRAS mutations in rectal cancer effectively

by Gan Y, Hu Q (...) Chen Z et 5 al. in Ann Surg Oncol #Surgery #SurgSky #GeneralSurgery #MedSky

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Frontiers | Silicone as a smart solution for simulating soft tissue—an iterative approach to developing a high-fidelity sustainable training model for laparoscopic appendectomy

Cost-effective silicone model enhances laparoscopic appendectomy training

by Roche AF, Diebold G (...) Condron CM et 5 al. in Front Surg #Surgery #SurgSky #GeneralSurgery #MedSky

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La narrativa de la inteligencia artificial aún está por escribirse, pero la forma en que gestionemos sus riesgos y posibilidades definirá el curso de la historia, dice @drjuliomayol.bsky.social aquí ⬇️

juliomayol.com/inteligencia...

#IA #InteligenciaArtificial

Embodied #AI

From #LLMs to #VLMs
@surgai.bsky.social
#AI

Un poco harto

Simulation and training with #GPT
@surgai.bsky.social

Waiting for next week to do Spanish emergency surgery course in Urduliz hospital