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PURPOSES: We performed a conversation analysis of the speech conducted among the surgical team during three-dimensional (3D)-printed liver model navigation for thrice or more repeated hepatectomy (TMRH). METHODS: Seventeen patients underwent 3D-printed liver navigation surgery for TMRH. After transcription of the utterances recorded during surgery, the transcribed utterances were coded by the utterer, utterance object, utterance content, sensor, and surgical process during conversation. We then analyzed the utterances and clarified the association between the surgical process and conversation through the intraoperative reference of the 3D-printed liver. RESULTS: In total, 130 conversations including 1648 segments were recorded. Utterance coding showed that the operator/assistant, 3D-printed liver/real liver, fact check (F)/plan check (Pc), visual check/tactile check, and confirmation of planned resection or preservation target (T)/confirmation of planned or ongoing resection line (L) accounted for 791/857, 885/763, 1148/500, 1208/440, and 1304/344 segments, respectively. The utterance's proportions of assistants, F, F of T on 3D-printed liver, F of T on real liver, and Pc of L on 3D-printed liver were significantly higher during non-expert surgeries than during expert surgeries. Confirming the surgical process with both 3D-printed liver and real liver and performing planning using a 3D-printed liver facilitates the safe implementation of TMRH, regardless of the surgeon's experience. CONCLUSIONS: The present study, using a unique conversation analysis, provided the first evidence for the clinical value of 3D-printed liver for TMRH for anatomical guidance of non-expert surgeons.
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OBJECTIVES: In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS: A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION: We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.
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Colangiopancreatografia Retrógrada Endoscópica , Pancreatite , Humanos , Colangiopancreatografia Retrógrada Endoscópica/efeitos adversos , Colangiopancreatografia Retrógrada Endoscópica/métodos , Inteligência Artificial , Estudos Retrospectivos , Pancreatite/diagnóstico , Pancreatite/epidemiologia , Pancreatite/etiologia , Ductos Pancreáticos , Fatores de RiscoRESUMO
Since even subtle mucosal changes may be depicted using virtual endoscopy created by the three-dimensional reconstruction of MDCT images, we developed a novel diagnostic imaging system that integrates and displays virtual enteroscopy, curved planar reconstruction, and a virtual unfolded view, the width of which changes with increases/decreases in the inner luminal diameter. The system is also equipped with artificial intelligence that superimposes and displays depressed areas, generates an automatic small bowel centerline that connects fragmented small bowel regions, and performs electronic cleansing. We retrospectively evaluated the diagnostic performance of this system for small bowel lesions in Crohn's disease, which were divided into two groups: endoscopically-observable and endoscopically-unobservable. Lesion detection rates for stenoses, longitudinal ulcers with a cobblestone appearance, and scars were excellent in both groups. This system, when used in combination with endoscopy, shows slight mucosal changes in areas in which an endoscope cannot reach due to strictures, thereby extending the range of observation of the small bowel. This system is a useful diagnostic modality that has the capacity to assess mucosal healing and provide extraluminal information.
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PURPOSE: Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL). METHODS: This prospective multicenter study was conducted by the 28 institutions, and 478 patients were included in the analysis. Deep learning was used to create two predictive models of the overall postoperative complications and neurological complications, one of the major complications. These models were constructed by learning the patient's preoperative background, clinical symptoms, surgical procedures, and imaging findings. These logistic regression models were also created, and these accuracies were compared with those of the DLM. RESULTS: Overall complications were observed in 127 cases (26.6%). The accuracy of the DLM was 74.6 ± 3.7% for predicting the overall occurrence of complications, which was comparable to that of the logistic regression (74.1%). Neurological complications were observed in 48 cases (10.0%), and the accuracy of the DLM was 91.7 ± 3.5%, which was higher than that of the logistic regression (90.1%). CONCLUSION: A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.
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Aprendizado Profundo , Doenças do Sistema Nervoso , Ossificação do Ligamento Longitudinal Posterior , Humanos , Ossificação do Ligamento Longitudinal Posterior/diagnóstico por imagem , Ossificação do Ligamento Longitudinal Posterior/cirurgia , Ossificação do Ligamento Longitudinal Posterior/complicações , Resultado do Tratamento , Estudos Prospectivos , Vértebras Cervicais/diagnóstico por imagem , Vértebras Cervicais/cirurgia , Complicações Pós-Operatórias/epidemiologia , Complicações Pós-Operatórias/etiologia , Estudos Retrospectivos , Ligamentos Longitudinais/cirurgiaRESUMO
BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (â¼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.
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Neoplasias Colorretais/patologia , Excisão de Linfonodo/estatística & dados numéricos , Metástase Linfática/diagnóstico , Aprendizado de Máquina , Fatores Etários , Idoso , Colectomia/estatística & dados numéricos , Colo/diagnóstico por imagem , Colo/patologia , Colo/cirurgia , Colonoscopia/estatística & dados numéricos , Neoplasias Colorretais/diagnóstico , Neoplasias Colorretais/cirurgia , Feminino , Seguimentos , Humanos , Japão/epidemiologia , Linfonodos/diagnóstico por imagem , Linfonodos/patologia , Linfonodos/cirurgia , Metástase Linfática/terapia , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Curva ROC , Estudos Retrospectivos , Medição de Risco/métodos , Fatores de RiscoRESUMO
BACKGROUND AND AIMS: Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists. METHODS: This was a propensity score-matched prospective study that took place at a university hospital between July 2020 and December 2020. We recruited patients aged ≥20 years who were scheduled for colonoscopy. Patients with polyposis, inflammatory bowel disease, or incomplete colonoscopy were excluded. We used a regulatory-approved CADe system and conducted a propensity score matching-based comparison of the ADR between patients examined with and without CADe as the primary outcome. RESULTS: During the study period, 2261 patients underwent colonoscopy with the CADe system or routine colonoscopy, and 172 patients were excluded in accordance with the exclusion criteria. Thirty endoscopists (9 nonexperts and 21 experts) were involved in this study. Propensity score matching was conducted using 5 factors, resulting in 1836 patients included in the analysis (918 patients in each group). The ADR was significantly higher in the CADe group than in the control group (26.4% vs 19.9%, respectively; relative risk, 1.32; 95% confidence interval, 1.12-1.57); however, there was no significant increase in the advanced neoplasia detection rate (3.7% vs 2.9%, respectively). CONCLUSIONS: The use of the CADe system for colonoscopy significantly increased the ADR in a large-scale prospective study including 30 endoscopists (Clinical trial registration number: UMIN000040677.).
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Adenoma , Neoplasias Colorretais , Adenoma/diagnóstico por imagem , Inteligência Artificial , Colonoscopia , Neoplasias Colorretais/diagnóstico por imagem , Humanos , Pontuação de Propensão , Estudos ProspectivosRESUMO
BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible. METHODS: To develop the deep learning-based AI system, 56,668 independent colonoscopy images were obtained from 5 centers for use as training images. To validate the trained AI system, consecutive colonoscopy videos taken at a university hospital between October 2018 and January 2019 were searched to construct a database containing polyps with unbiased variance. All images were annotated by endoscopists according to the presence or absence of polyps and the polyps' locations with bounding boxes. RESULTS: A total of 1405 videos acquired during the study period were identified for the validation database, 797 of which contained at least 1 polyp. Of these, 100 videos containing 100 independent polyps and 13 videos negative for polyps were randomly extracted, resulting in 152,560 frames (49,799 positive frames and 102,761 negative frames) for the database. The AI showed 90.5% sensitivity and 93.7% specificity for frame-based analysis. The per-polyp sensitivities for all, diminutive, protruded, and flat polyps were 98.0%, 98.3%, 98.5%, and 97.0%, respectively. CONCLUSIONS: Our trained AI system was validated with a new large publicly accessible colonoscopy database and could identify colorectal lesions with high sensitivity and specificity. (Clinical trial registration number: UMIN 000037064.).
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Adenoma , Pólipos do Colo , Adenoma/diagnóstico por imagem , Inteligência Artificial , Pólipos do Colo/diagnóstico por imagem , Colonoscopia , Computadores , HumanosRESUMO
The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.
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Adenoma , Pólipos do Colo , Neoplasias Colorretais , Inteligência Artificial , Ceco , Colonoscopia , Neoplasias Colorretais/diagnóstico por imagem , HumanosRESUMO
BACKGROUND & AIMS: Precise optical diagnosis of colorectal polyps could improve the cost-effectiveness of colonoscopy and reduce polypectomy-related complications. However, it is difficult for community-based non-experts to obtain sufficient diagnostic performance. Artificial intelligence-based systems have been developed to analyze endoscopic images; they identify neoplasms with high accuracy and low interobserver variation. We performed a multi-center study to determine the diagnostic accuracy of EndoBRAIN, an artificial intelligence-based system that analyzes cell nuclei, crypt structure, and microvessels in endoscopic images, in identification of colon neoplasms. METHODS: The EndoBRAIN system was initially trained using 69,142 endocytoscopic images, taken at 520-fold magnification, from patients with colorectal polyps who underwent endoscopy at 5 academic centers in Japan from October 2017 through March 2018. We performed a retrospective comparative analysis of the diagnostic performance of EndoBRAIN vs that of 30 endoscopists (20 trainees and 10 experts); the endoscopists assessed images from 100 cases produced via white-light microscopy, endocytoscopy with methylene blue staining, and endocytoscopy with narrow-band imaging. EndoBRAIN was used to assess endocytoscopic, but not white-light, images. The primary outcome was the accuracy of EndoBrain in distinguishing neoplasms from non-neoplasms, compared with that of endoscopists, using findings from pathology analysis as the reference standard. RESULTS: In analysis of stained endocytoscopic images, EndoBRAIN identified colon lesions with 96.9% sensitivity (95% CI, 95.8%-97.8%), 100% specificity (95% CI, 99.6%-100%), 98% accuracy (95% CI, 97.3%-98.6%), a 100% positive-predictive value (95% CI, 99.8%-100%), and a 94.6% negative-predictive (95% CI, 92.7%-96.1%); these values were all significantly greater than those of the endoscopy trainees and experts. In analysis of narrow-band images, EndoBRAIN distinguished neoplastic from non-neoplastic lesions with 96.9% sensitivity (95% CI, 95.8-97.8), 94.3% specificity (95% CI, 92.3-95.9), 96.0% accuracy (95% CI, 95.1-96.8), a 96.9% positive-predictive value, (95% CI, 95.8-97.8), and a 94.3% negative-predictive value (95% CI, 92.3-95.9); these values were all significantly higher than those of the endoscopy trainees, sensitivity and negative-predictive value were significantly higher but the other values are comparable to those of the experts. CONCLUSIONS: EndoBRAIN accurately differentiated neoplastic from non-neoplastic lesions in stained endocytoscopic images and endocytoscopic narrow-band images, when pathology findings were used as the standard. This technology has been authorized for clinical use by the Japanese regulatory agency and should be used in endoscopic evaluation of small polyps more widespread clinical settings. UMIN clinical trial no: UMIN000028843.
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Pólipos do Colo , Neoplasias Colorretais , Inteligência Artificial , Colonoscopia , Neoplasias Colorretais/diagnóstico , Humanos , Imagem de Banda Estreita , Estudos Retrospectivos , Sensibilidade e EspecificidadeRESUMO
Background: Accurate registration for surgical navigation of laparoscopic surgery is highly challenging due to vessel deformation. Here, we describe the design of a deformable model with improved matching accuracy by applying the finite element method (FEM).Material and methods: ANSYS software was used to simulate an FEM model of the vessel after pull-up based on laparoscopic gastrectomy requirements. The central line of the FEM model and the central line of the ground truth were drawn and compared. Based on the material and parameters determined from the animal experiment, a perigastric vessel FEM model of a gastric cancer patient was created, and its accuracy in a laparoscopic gastrectomy surgical scene was evaluated.Results: In the animal experiment, the FEM model created with Ogden foam material exhibited better results. The average distance between the two central lines was 6.5mm, and the average distance between their closest points was 3.8 mm. In the laparoscopic gastrectomy surgical scene, the FEM model and the true artery deformation demonstrated good coincidence.Conclusion: In this study, a deformable vessel model based on FEM was constructed using preoperative CT images to improve matching accuracy and to supply a reference for further research on deformation matching to facilitate laparoscopic gastrectomy navigation.
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Análise de Elementos Finitos , Gastrectomia/métodos , Artéria Gástrica/anatomia & histologia , Laparoscopia/métodos , Neoplasias Gástricas/cirurgia , Animais , Artéria Gástrica/diagnóstico por imagem , Humanos , Masculino , Suínos , Tomografia Computadorizada por Raios XRESUMO
BACKGROUND AND AIMS: In the treatment of ulcerative colitis (UC), an incremental benefit of achieving histologic healing beyond that of endoscopic mucosal healing has been suggested; persistent histologic inflammation increases the risk of exacerbation and dysplasia. However, identification of persistent histologic inflammation is extremely difficult using conventional endoscopy. Furthermore, the reproducibility of endoscopic disease activity is poor. We developed and evaluated a computer-aided diagnosis (CAD) system to predict persistent histologic inflammation using endocytoscopy (EC; 520-fold ultra-magnifying endoscope). METHODS: We evaluated the accuracy of the CAD system using test image sets. First, we retrospectively reviewed the data of 187 patients with UC from whom biopsy samples were obtained after endocytoscopic observation. EC images and biopsy samples of each patient were collected from 6 colorectal segments: cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. All EC images were tagged with reference to the biopsy sample's histologic activity. For validation samples, 525 validation sets of 525 independent segments were collected from 100 patients, and 12,900 EC images from the remaining 87 patients were used for machine learning to construct CAD. The primary outcome measure was the diagnostic ability of CAD to predict persistent histologic inflammation. Its reproducibility for all test images was also assessed. RESULTS: CAD provided diagnostic sensitivity, specificity, and accuracy as follows: 74% (95% confidence interval, 65%-81%), 97% (95% confidence interval, 95%-99%), and 91% (95% confidence interval, 83%-95%), respectively. Its reproducibility was perfect (κ = 1). CONCLUSIONS: Our CAD system potentially allows fully automated identification of persistent histologic inflammation associated with UC.
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Algoritmos , Colite Ulcerativa/patologia , Colo/patologia , Diagnóstico por Computador/métodos , Inflamação/patologia , Mucosa Intestinal/patologia , Aprendizado de Máquina , Reto/patologia , Inteligência Artificial , Automação , Colite Ulcerativa/diagnóstico , Colonoscopia , Feminino , Humanos , Inflamação/diagnóstico , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sensibilidade e EspecificidadeRESUMO
Background: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost. Objective: To evaluate the performance of real-time CAD with endocytoscopes (×520 ultramagnifying colonoscopes providing microvascular and cellular visualization of colorectal polyps after application of the narrow-band imaging [NBI] and methylene blue staining modes, respectively). Design: Single-group, open-label, prospective study. (UMIN [University hospital Medical Information Network] Clinical Trial Registry: UMIN000027360). Setting: University hospital. Participants: 791 consecutive patients undergoing colonoscopy and 23 endoscopists. Intervention: Real-time use of CAD during colonoscopy. Measurements: CAD-predicted pathology (neoplastic or nonneoplastic) of detected diminutive polyps (≤5 mm) on the basis of real-time outputs compared with pathologic diagnosis of the resected specimen (gold standard). The primary end point was whether CAD with the stained mode produced a negative predictive value (NPV) of 90% or greater for identifying diminutive rectosigmoid adenomas, the threshold required to "diagnose-and-leave" nonneoplastic polyps. Best- and worst-case scenarios assumed that polyps lacking either CAD diagnosis or pathology were true- or false-positive or true- or false-negative, respectively. Results: Overall, 466 diminutive (including 250 rectosigmoid) polyps from 325 patients were assessed by CAD, with a pathologic prediction rate of 98.1% (457 of 466). The NPVs of CAD for diminutive rectosigmoid adenomas were 96.4% (95% CI, 91.8% to 98.8%) (best-case scenario) and 93.7% (CI, 88.3% to 97.1%) (worst-case scenario) with stained mode and 96.5% (CI, 92.1% to 98.9%) (best-case scenario) and 95.2% (CI, 90.3% to 98.0%) (worst-case scenario) with NBI. Limitation: Two thirds of the colonoscopies were conducted by experts who had each experienced more than 200 endocytoscopies; 186 polyps not assessed by CAD were excluded. Conclusion: Real-time CAD can achieve the performance level required for a diagnose-and-leave strategy for diminutive, nonneoplastic rectosigmoid polyps. Primary Funding Source: Japan Society for the Promotion of Science.
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Adenoma/diagnóstico , Inteligência Artificial , Pólipos do Colo/diagnóstico , Colonoscopia/métodos , Diagnóstico por Computador/métodos , Adenoma/patologia , Idoso , Pólipos do Colo/patologia , Corantes , Estudos de Viabilidade , Feminino , Humanos , Masculino , Azul de Metileno , Pessoa de Meia-Idade , Imagem de Banda Estreita , Estudos Prospectivos , Sensibilidade e EspecificidadeRESUMO
With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early-stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false-negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real-time gastroscopy to provide on-site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.
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Inteligência Artificial , Diagnóstico por Computador , Doenças do Sistema Digestório/diagnóstico , Endoscopia do Sistema Digestório/métodos , Detecção Precoce de Câncer , HumanosRESUMO
BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. METHODS AND RESULTS: Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. CONCLUSION: We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence-assisted medical devices.
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Inteligência Artificial , Pólipos do Colo/diagnóstico , Colonoscopia/normas , Neoplasias Colorretais/diagnóstico , Diagnóstico por Computador/normas , Erros de Diagnóstico/prevenção & controle , Previsões , Humanos , Melhoria de QualidadeRESUMO
Background and study aims Invasive cancer carries the risk of metastasis, and therefore, the ability to distinguish between invasive cancerous lesions and less-aggressive lesions is important. We evaluated a computer-aided diagnosis system that uses ultra-high (approximatelyâ×â400) magnification endocytoscopy (EC-CAD). Patients and methods We generated an image database from a consecutive series of 5843 endocytoscopy images of 375 lesions. For construction of a diagnostic algorithm, 5543 endocytoscopy images from 238 lesions were randomly extracted from the database for machine learning. We applied the obtained algorithm to 200 endocytoscopy images and calculated test characteristics for the diagnosis of invasive cancer. We defined a high-confidence diagnosis as having aâ≥â90â% probability of being correct. Results Of the 200 test images, 188 (94.0â%) were assessable with the EC-CADâsystem. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were 89.4â%, 98.9â%, 94.1â%, 98.8â%, and 90.1â%, respectively. High-confidence diagnosis had a sensitivity, specificity, accuracy, PPV, and NPV of 98.1â%, 100â%, 99.3â%, 100â%, and 98.8â%, respectively. Conclusion: EC-CADâmay be a useful tool in diagnosing invasive colorectal cancer.
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Colonoscopia , Neoplasias Colorretais/diagnóstico por imagem , Neoplasias Colorretais/patologia , Diagnóstico por Computador , Idoso , Algoritmos , Corantes , Citodiagnóstico/métodos , Feminino , Violeta Genciana , Humanos , Microscopia Intravital , Aprendizado de Máquina , Masculino , Azul de Metileno , Pessoa de Meia-Idade , Invasividade Neoplásica , Valor Preditivo dos Testes , Estudos RetrospectivosRESUMO
Background and study aims: Optical diagnosis of colorectal polyps is expected to improve the cost-effectiveness of colonoscopy, but achieving a high accuracy is difficult for trainees. Computer-aided diagnosis (CAD) is therefore receiving attention as an attractive tool. This study aimed to validate the efficacy of the latest CADâmodel for endocytoscopy (380-fold ultra-magnifying endoscopy). Patients and methods: This international web-based trial was conducted between August and November 2015.âA web-based test comprising one white-light and one endocytoscopic image of 205 small colorectal polyps (≤â10âmm) from 123 patients was undertaken by both CADâand by endoscopists (three experts and ten non-experts from three countries). Outcome measures were accuracy in identifying neoplastic change in diminutive (≤â5âmm) and small (≤â10âmm) polyps, and accuracy in predicting post-polypectomy surveillance intervals according to current guidelines for high confidence optical diagnoses of diminutive polyps. Results: Of the 205 small polyps (147 neoplastic and 58 non-neoplastic), 139 were diminutive. CADâwas accurate for 89â% (95â% confidence interval [CI] 83â%â-â94â%) of diminutive polyps and 89â% (84â%â-â93â%) of small polyps, which was significantly greater than results for the non-experts (73â% [71â%â-â76â%], Pâ<â0.001; and 76â% [74â%â-â78â%], Pâ<â0.001, respectively) and comparable with the experts' results (90â% [87â%â-â93â%], Pâ=â0.703; and 91â% [89â%â-â93â%], Pâ=â0.106, respectively). The surveillance interval predicted by CADâprovided 98â% (93â%â-â100â%) and 96â% (91â%â-â99â%) agreement with pathology-directed intervals of the European and American guidelines, respectively. Conclusions: The use of CADâin endocytoscopy can be effective in the management of diminutive/small colorectal polyps.UMIN Clinical Trial Registry: UMIN000018185.
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Adenoma/diagnóstico por imagem , Neoplasias do Colo/diagnóstico por imagem , Pólipos do Colo/diagnóstico por imagem , Diagnóstico por Computador , Vigilância da População , Neoplasias Retais/diagnóstico por imagem , Adenoma/patologia , Idoso , Neoplasias do Colo/patologia , Pólipos do Colo/patologia , Colonoscopia , Feminino , Humanos , Internacionalidade , Internet , Masculino , Pessoa de Meia-Idade , Variações Dependentes do Observador , Imagem Óptica , Guias de Prática Clínica como Assunto , Neoplasias Retais/patologia , Carga TumoralAssuntos
Adenoma/diagnóstico por imagem , Inteligência Artificial , Neoplasias do Colo/diagnóstico por imagem , Pólipos do Colo/diagnóstico por imagem , Colonoscopia/métodos , Idoso , Colo/diagnóstico por imagem , Colonoscopia/efeitos adversos , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Masculino , Erros Médicos/prevenção & controle , Pessoa de Meia-Idade , Projetos PilotoRESUMO
The task of segmentation is integral to computer-aided surgery systems. Given the privacy concerns associated with medical data, collecting a large amount of annotated data for training is challenging. Unsupervised learning techniques, such as contrastive learning, have shown powerful capabilities in learning image-level representations from unlabelled data. This study leverages classification labels to enhance the accuracy of the segmentation model trained on limited annotated data. The method uses a multi-scale projection head to extract image features at various scales. The partitioning method for positive sample pairs is then improved to perform contrastive learning on the extracted features at each scale to effectively represent the differences between positive and negative samples in contrastive learning. Furthermore, the model is trained simultaneously with both segmentation labels and classification labels. This enables the model to extract features more effectively from each segmentation target class and further accelerates the convergence speed. The method was validated using the publicly available CholecSeg8k dataset for comprehensive abdominal cavity surgical segmentation. Compared to select existing methods, the proposed approach significantly enhances segmentation performance, even with a small labelled subset (1-10%) of the dataset, showcasing a superior intersection over union (IoU) score.
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A 71-year-old man was admitted because of acute exacerbation of interstitial pneumonia following a right upper lobectomy for lung cancer. His respiratory failure worsened after admission, and he required mechanical ventilation. He was undergoing intensive immunosuppressive treatment, including high-dose corticosteroids and cyclosporine, and had watery diarrhea six times a day. White blood cells were found in the stool, and an intestinal infection was suspected. Fecal cultures showed no pathogenic bacteria. Multiplex polymerase chain reaction for gastrointestinal infection yielded negative results. Based on the increasing number of cytomegalovirus (CMV) antigen-positive cells in the CMV antigenemia assay, we suspected CMV colitis. However, the patient was still undergoing mechanical ventilation, and colonoscopy was difficult to perform. After explaining the procedure to the patient and obtaining his consent, the BioFire® FilmArray® Meningitis/Encephalitis (ME) Panel was performed using a fecal specimen. CMV was detected. Intravenous infusion of ganciclovir at 5 mg/kg was immediately commenced and administered every 12 hours for three weeks. Intravenous infusion at 5 mg/kg was continued every 24 hours thereafter for a further three weeks. When CMV colitis is suspected but the patient's condition prevents tissue collection through colonoscopy and standard diagnosis by histopathology, the addition of CMV PCR using a stool sample may assist in the clinical diagnosis of CMV colitis. The use of multiplex polymerase chain reaction is expected to contribute to prompt and appropriate treatment.
RESUMO
PURPOSE: This paper considers a new problem setting for multi-organ segmentation based on the following observations. In reality, (1) collecting a large-scale dataset from various institutes is usually impeded due to privacy issues; (2) many images are not labeled since the slice-by-slice annotation is costly; and (3) datasets may exhibit inconsistent, partial annotations across different institutes. Learning a federated model from these distributed, partially labeled, and unlabeled samples is an unexplored problem. METHODS: To simulate this multi-organ segmentation problem, several distributed clients and a central server are maintained. The central server coordinates with clients to learn a global model using distributed private datasets, which comprise a small part of partially labeled images and a large part of unlabeled images. To address this problem, a practical framework that unifies partially supervised learning (PSL), semi-supervised learning (SSL), and federated learning (FL) paradigms with PSL, SSL, and FL modules is proposed. The PSL module manages to learn from partially labeled samples. The SSL module extracts valuable information from unlabeled data. Besides, the FL module aggregates local information from distributed clients to generate a global statistical model. With the collaboration of three modules, the presented scheme could take advantage of these distributed imperfect datasets to train a generalizable model. RESULTS: The proposed method was extensively evaluated with multiple abdominal CT datasets, achieving an average result of 84.83% in Dice and 41.62 mm in 95HD for multi-organ (liver, spleen, and stomach) segmentation. Moreover, its efficacy in transfer learning further demonstrated its good generalization ability for downstream segmentation tasks. CONCLUSION: This study considers a novel problem of multi-organ segmentation, which aims to develop a generalizable model using distributed, partially labeled, and unlabeled CT images. A practical framework is presented, which, through extensive validation, has proved to be an effective solution, demonstrating strong potential in addressing this challenging problem.