Explore the vast range of titles available.

BackgroundAlthough a number of robot-assisted minimally invasive esophagectomy (RAMIE) procedures have been performed due to three-dimensional field of view, image stabilization, and flexible joint function, both the surgeons and surgical teams require proficiency. This study aimed to establish an artificial intelligence (AI)-based automated surgical-phase recognition system for RAMIE by analyzing robotic surgical videos.MethodsThis study enrolled 31 patients who underwent RAMIE. The videos were annotated into the following nine surgical phases: preparation, lower mediastinal dissection, upper mediastinal dissection, azygos vein division, subcarinal lymph node dissection (LND), right recurrent laryngeal nerve (RLN) LND, left RLN LND, esophageal transection, and post-dissection to completion of surgery to train the AI for automated phase recognition. An additional phase (“no step”) was used to indicate video sequences upon removal of the camera from the thoracic cavity. All the patients were divided into two groups, namely, early period (20 patients) and late period (11 patients), after which the relationship between the surgical-phase duration and the surgical periods was assessed.ResultsFourfold cross validation was applied to evaluate the performance of the current model. The AI had an accuracy of 84%. The preparation (p = 0.012), post-dissection to completion of surgery (p = 0.003), and “no step” (p < 0.001) phases predicted by the AI were significantly shorter in the late period than in the early period.ConclusionsA highly accurate automated surgical-phase recognition system for RAMIE was established using deep learning. Specific phase durations were significantly associated with the surgical period at the authors’ institution.

BackgroundAlthough radical gastrectomy with lymph node dissection is the standard treatment for gastric cancer, the complication rate remains high. Thus, estimation of surgical complexity is required for safety. We aim to investigate the association between the surgical process and complexity, such as a risk of complications in robotic distal gastrectomy (RDG), to establish an artificial intelligence (AI)-based automated surgical phase recognition by analyzing robotic surgical videos, and to investigate the predictability of surgical complexity by AI.MethodThis study assessed clinical data and robotic surgical videos for 56 patients who underwent RDG for gastric cancer. We investigated (1) the relationship between surgical complexity and perioperative factors (patient characteristics, surgical process); (2) AI training for automated phase recognition and model performance was assessed by comparing predictions to the surgeon-annotated reference; (3) AI model predictability for surgical complexity was calculated by the area under the curve.ResultSurgical complexity score comprised extended total surgical duration, bleeding, and complications and was strongly associated with the intraoperative surgical process, especially in the beginning phases (area under the curve 0.913). We established an AI model that can recognize surgical phases from video with 87% accuracy; AI can determine intraoperative surgical complexity by calculating the duration of beginning phases from phases 1–3 (area under the curve 0.859).ConclusionSurgical complexity, as a surrogate of short-term outcomes, can be predicted by the surgical process, especially in the extended duration of beginning phases. Surgical complexity can also be evaluated with automation using our artificial intelligence-based model.

Tumor stiffness is drawing attention as a novel axis that is orthogonal to existing parameters such as pathological examination. We developed a new diagnostic method that focuses on differences in stiffness between tumor and normal tissue. This study comprised a clinical application of laser resonance frequency analysis (L‐RFA) for diagnosing gastric cancer. L‐RFA allows for precise and contactless evaluation of tissue stiffness. We used a laser to create vibrations on a sample's surface that were then measured using a vibrometer. The data were averaged and analyzed to enhance accuracy. In the agarose phantom experiments, a clear linear correlation was observed between the Young's modulus of the phantoms (0.34–0.71 MPa) and the summation of normalized vibration peaks (Score) in the 1950–4050 Hz range (R2 = 0.93145). Higher Young's moduli also resulted in lower vibration peaks at the excitation frequency, signal‐to‐noise (S/N) ratios, and harmonic peaks. We also conducted L‐RFA measurements on gastric cancer specimens from two patients who underwent robot‐assisted distal gastrectomy. Our results revealed significant waveform differences between tumor and normal regions, similar to the findings in agarose phantoms, with tumor regions exhibiting lower vibration peaks at the excitation frequency, S/N ratios, and harmonic peaks. Statistical analysis confirmed significant differences in the score between normal and tumor regions (p = 0.00354). L‐RFA was able to assess tumor stiffness and holds great promise as a noninvasive diagnostic tool for gastric cancer. This study aimed to differentiate the stiffness between tumor and normal stomach tissue using the laser resonance frequency analysis technique, which measures an object's vibratory characteristics with a laser. As a result, there were significant waveform differences between the tumor and normal regions of the resected gastric cancer specimens. This technology is expected to help improve current methods for diagnosing gastric cancer, which can be difficult endoscopically.

BackgroundDetecting pathological complete response (pCR) before surgery would facilitate nonsurgical approach after neoadjuvant chemotherapy (NAC). We developed an artificial intelligence (AI)-guided pCR evaluation using a deep neural network to identify pCR before surgery.MethodsThis study examined resectable esophageal squamous cell carcinoma (ESCC) patients who underwent esophagectomy after NAC. The same number of histological responders without pCR and non-responders were randomly selected based on the number of pCR patients. Endoscopic images were analyzed using a deep neural network. A test dataset consisting of 20 photos was used for validation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of AI and four experienced endoscopists' pCR evaluations were calculated. For pathological response evaluation, Japanese Classification of Esophageal Cancer was used.ResultsThe study enrolled 123 patients, including 41 patients with pCR, the same number of histological responders without pCR, and non-responders [grade 0, 5 (4%); grade 1a, 36 (30%); grade 1b, 21 (17%); grade 2, 20 (16%); grade 3, 41 (33%)]. In 20 models, the median values of sensitivity, specificity, PPV, NPV, and accuracy for endoscopic response (ER) detection were 60%, 81%, 77%, 67%, and 70%, respectively. Similarly, the endoscopists’ median of these was 43%, 90%, 85%, 65%, and 66%, respectively.ConclusionsThis proof-of-concept study demonstrated that the AI-guided endoscopic response evaluation after NAC could identify pCR with moderate accuracy. The current AI algorithm might guide an individualized treatment strategy including nonsurgical approach in ESCC patients through prospective studies with careful external validation to demonstrate the clinical value of this diagnostic approach including primary tumor and lymph node.

Background Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employing deep learning technology has been attempted and established for esophageal cancer so far. Purpose To establish an AI-based diagnostic system for esophageal cancer from CT images. Materials and methods In this single-center, retrospective cohort study, 457 patients with primary esophageal cancer referred to our division between 2005 and 2018 were enrolled. We fine-tuned VGG16, an image recognition model of deep learning convolutional neural network (CNN), for the detection of esophageal cancer. We evaluated the diagnostic accuracy of the CNN using a test data set including 46 cancerous CT images and 100 non-cancerous images and compared it to that of two radiologists. Results Pre-treatment esophageal cancer stages of the patients included in the test data set were clinical T1 (12 patients), clinical T2 (9 patients), clinical T3 (20 patients), and clinical T4 (5 patients). The CNN-based system showed a diagnostic accuracy of 84.2%, F value of 0.742, sensitivity of 71.7%, and specificity of 90.0%. Conclusions Our AI-based diagnostic system succeeded in detecting esophageal cancer with high accuracy. More training with vast datasets collected from multiples centers would lead to even higher diagnostic accuracy and aid better decision making.

Delirium is an important prognostic factor in postoperative patients undergoing cardiovascular surgery and intervention, including transcatheter aortic valve implantation (TAVI). However, delirium after transcatheter aortic valve implantation (DAT) is difficult to predict and its pathophysiology is still unclear. We aimed to investigate whether preoperative cerebral blood flow (CBF) is associated with DAT and, if so, whether CBF measurement is useful for predicting DAT. We evaluated CBF in 50 consecutive patients before TAVI (84.7±4.5 yrs., 36 females) using .sup.99m Tc ethyl cysteinate dimer single-photon emission computed tomography. Preoperative CBF of the DAT group (N = 12) was compared with that of the non-DAT group (N = 38) using whole brain voxel-wise analysis with SPM12 and region of interest-based analysis with the easy-Z score imaging system. Multivariable logistic regression analysis with the presence of DAT was used to create its prediction model. The whole brain analysis showed that preoperative CBF in the insula was lower in the DAT than in the non-DAT group (P<0.05, family-wise error correction). Decrease extent ratio in the insula of the DAT group (17.6±11.5%) was also greater relative to that of the non-DAT group (7.0±11.3%) in the region of interest-based analysis (P = 0.007). A model that included preoperative CBF in the insula and conventional indicators (frailty index, short physical performance battery and mini-mental state examination) showed the best predictive power for DAT (AUC 0.882). These results suggest that preoperative CBF in the insula is associated with DAT and may be useful for its prediction.

BackgroundWe previously reported that endoscopic response evaluation can preoperatively predict the prognosis and distribution of residual tumors after neoadjuvant chemotherapy (NAC). In this study, we developed artificial intelligence (AI)-guided endoscopic response evaluation using a deep neural network to discriminate endoscopic responders (ERs) in patients with esophageal squamous cell carcinoma (ESCC) after NAC.MethodSurgically resectable ESCC patients who underwent esophagectomy following NAC were retrospectively analyzed in this study. Endoscopic images of the tumors were analyzed using a deep neural network. The model was validated with a test data set using 10 newly collected ERs and 10 newly collected non-ER images. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the endoscopic response evaluation by AI and endoscopists were calculated and compared.ResultsOf 193 patients, 40 (21%) were diagnosed as ERs. The median sensitivity, specificity, PPV, and NPV values for ER detection in 10 models were 60%, 100%, 100%, and 71%, respectively. Similarly, the median values by the endoscopist were 80%, 80%, 81%, and 81%, respectively.ConclusionThis proof-of-concept study using a deep learning algorithm demonstrated that the constructed AI-guided endoscopic response evaluation after NAC could identify ER with high specificity and PPV. It would appropriately guide an individualized treatment strategy that includes an organ preservation approach in ESCC patients.