Objective Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies. Methods The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review. Results As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users. Conclusions This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.

Application of artificial intelligence in medical technologies: A systematic review of main trends

Due to the increasing prevalence of high myopia around the world, structural and functional damages to the optic nerve in high myopia has recently attracted much attention. Evidence has shown that high myopia is related to the development of glaucomatous or glaucoma-like optic neuropathy, and that both have many common features. These similarities often pose a diagnostic challenge that will affect the future management of glaucoma suspects in high myopia. In this review, we summarize similarities and differences in optic neuropathy arising from non-pathologic high myopia and glaucoma by considering their respective structural and functional characteristics on fundus photography, optical coherence tomography scanning, and visual field tests. These features may also help to distinguish the underlying mechanisms of the optic neuropathies and to determine management strategies for patients with high myopia and glaucoma. 

Optic neuropathy in high myopia: Glaucoma or high myopia or both?

Laparoscopic inguinal hernia repair in bladder exstrophy, a new modified solution to an old problem: A cohort study

Abstract Artificial Intelligence (AI) has been developed and implemented in healthcare with the valuable potential to reduce health, social, and economic inequities, help actualize universal health coverage, and improve health outcomes on a global scale. The application of AI in emergency surgery settings could improve clinical practice and operating rooms management by promoting consistent, high-quality decision making while preserving the importance of bedside assessment and human intuition as well as respect for human rights and equitable surgical care, but ethical and legal issues are slowing down surgeons’ enthusiasm. Emergency surgeons are aware that prioritizing education, increasing the availability of high AI technologies for emergency and trauma surgery, and funding to support research projects that use AI to provide decision support in the operating room are crucial to create an emergency “intelligent” surgery. 

/pdf/artificial-intelligence-in-surgery-the-emergency-surgeons-3fj858gf.pdf

Artificial intelligence in surgery: the emergency surgeon’s perspective (the ARIES project)

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models that predict whether learners might be at risk. However, the question on how teacher-like is the model behaving under this detection task still remains unanswered. In this paper, we investigate the (dis)agreement between teachers and model decisions, using a real-world flipped course as a case study. From the model perspective, we considered a well-known neural network, trained on educational indicators extracted from online pre-class logs. To gather teachers’ understanding, we employed a crowd sourcing approach including over 360 human intelligence tasks from 60 university teachers. We asked each recruited teacher to analyze visualizations pertaining to four relevant educational indicators of a given learner, and reason about their probability of failing the course (and so requiring support). Learners presented to teachers were selected to address different aspects of model confidence and (in)accuracy. Our results show that teacher and model predictions diverged for students who passed the course, while predictions were similar for students who failed the course. Moreover, confidence and correctness were more aligned in teachers than the model, reducing the unknown risks originally present in models. The source code is available at https://github.com/epfl-ml4ed/unknown-unknowns.

/pdf/how-close-are-predictive-models-to-teachers-in-detecting-1vtazl91.pdf

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

Background: The occurrence of biliary duct injury (BDI) after laparoscopic cholecystectomy (LC) remains 0.2-1.5%, which is largely caused by anatomic misidentifications. To solve this problem, we developed an artificial intelligence model, SurgSmart, and preliminarily verified its potential surgical guidance ability by comparing its performance with surgeons. Methods: We prospectively collected 60 LC videos from November 2019 to August 2020 and enrolled 41 videos into the model establishment. Four important anatomic regions, namely cystic duct, cystic artery, common bile duct, and cystic plate, were annotated, and YOLOv3 (You Look Only Once), an object detection algorithm, was applied to develop the model SurgSmart. To further evaluate its performance, comparisons were made among SurgSmart, trainees, and seniors (surgical experience in LC > 100). Results: In total, 101,863 frames were extracted from videos, and 5533 video frames were selected, annotated, and used in model training. The mean average precision (mAP) of SurgSmart was 0.710. Comparative results show SurgSmart had significantly higher intersection-over-union (IoU) and accuracy (IoU ≥ 0.5) in anatomy detection than those of seniors (n = 36) and trainees (n = 32) despite the existence of severe inflammation. Additionally, SurgSmart tended to correctly identify anatomic regions in earlier surgical phases than most of the seniors and trainees (P < 0.001). Conclusions: SurgSmart is not only capable of accurately detecting and positioning anatomic regions in LC but also has better performance than that of the trainees and seniors in terms of individual still images and the whole set.

Artificial intelligence in laparoscopic cholecystectomy: does computer vision outperform human vision?

PurposeTo develop and validate the performance of a high myopia (HM)-specific normative database of peripapillary retinal nerve fiber layer (pRNFL) thickness in differentiating HM from highly myopic glaucoma (HMG).DesignCross-sectional multicenter study.ParticipantsA total of 1367 Chinese participants (2325 eyes) with nonpathologic HM or HMG were included from 4 centers. After quality control, 1108 eyes from 694 participants with HM were included in the normative database; 459 eyes from 408 participants (323 eyes with HM and 136 eyes with HMG) and 322 eyes from 197 participants (131 eyes with HM and 191 eyes with HMG) were included in the internal and external validation sets, respectively. Only HMG eyes with an intraocular pressure > 21 mmHg were included.MethodsThe pRNFL thickness was measured with swept-source (SS) OCT. Four strategies of pRNFL-specified values were examined, including global and quadrantic pRNFL thickness below the lowest fifth or the lowest first percentile of the normative database.Main Outcomes MeasuresThe accuracy, sensitivity, and specificity of the HM-specific normative database for detecting HMG.ResultsSetting the fifth percentile of the global pRNFL thickness as the threshold, using the HM-specific normative database, we achieved an accuracy of 0.93 (95% confidence interval [CI], 0.90–0.95) and 0.85 (95% CI, 0.81–0.89), and, using the first percentile as the threshold, we acheived an accuracy of 0.85 (95% CI, 0.81–0.88) and 0.70 (95% CI, 0.65–0.75) in detecting HMG in the internal and external validation sets, respectively. The fifth percentile of the global pRNFL thickness achieved high sensitivities of 0.75 (95% CI, 0.67–0.82) and 0.75 (95% CI, 0.68–0.81) and specificities of 1.00 (95% CI, 0.99–1.00) and 1.00 (95% CI, 0.97–1.00) in the internal and external validation datasets, respectively. Compared with the built-in database of the OCT device, the HM-specific normative database showed a higher sensitivity and specificity than the corresponding pRNFL thickness below the fifth or first percentile (P < 0.001 for all).ConclusionsThe HM-specific normative database is more capable of detecting HMG eyes than the SS OCT built-in database, which may be an effective tool for differential diagnosis between HMG and HM.Financial Disclosure(s)Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. To develop and validate the performance of a high myopia (HM)-specific normative database of peripapillary retinal nerve fiber layer (pRNFL) thickness in differentiating HM from highly myopic glaucoma (HMG). Cross-sectional multicenter study. A total of 1367 Chinese participants (2325 eyes) with nonpathologic HM or HMG were included from 4 centers. After quality control, 1108 eyes from 694 participants with HM were included in the normative database; 459 eyes from 408 participants (323 eyes with HM and 136 eyes with HMG) and 322 eyes from 197 participants (131 eyes with HM and 191 eyes with HMG) were included in the internal and external validation sets, respectively. Only HMG eyes with an intraocular pressure > 21 mmHg were included. The pRNFL thickness was measured with swept-source (SS) OCT. Four strategies of pRNFL-specified values were examined, including global and quadrantic pRNFL thickness below the lowest fifth or the lowest first percentile of the normative database. The accuracy, sensitivity, and specificity of the HM-specific normative database for detecting HMG. Setting the fifth percentile of the global pRNFL thickness as the threshold, using the HM-specific normative database, we achieved an accuracy of 0.93 (95% confidence interval [CI], 0.90–0.95) and 0.85 (95% CI, 0.81–0.89), and, using the first percentile as the threshold, we acheived an accuracy of 0.85 (95% CI, 0.81–0.88) and 0.70 (95% CI, 0.65–0.75) in detecting HMG in the internal and external validation sets, respectively. The fifth percentile of the global pRNFL thickness achieved high sensitivities of 0.75 (95% CI, 0.67–0.82) and 0.75 (95% CI, 0.68–0.81) and specificities of 1.00 (95% CI, 0.99–1.00) and 1.00 (95% CI, 0.97–1.00) in the internal and external validation datasets, respectively. Compared with the built-in database of the OCT device, the HM-specific normative database showed a higher sensitivity and specificity than the corresponding pRNFL thickness below the fifth or first percentile (P < 0.001 for all). The HM-specific normative database is more capable of detecting HMG eyes than the SS OCT built-in database, which may be an effective tool for differential diagnosis between HMG and HM. 

High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in Chinese Population.

Background Hippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task. Objective Our aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS. Methods We proposed a deep learning algorithm called “HS-Net” to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility. Results The HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method. Conclusions Our computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.

/pdf/automated-detection-of-hippocampal-sclerosis-using-real-1azneldh.pdf

Automated detection of hippocampal sclerosis using real-world clinical MRI images

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Artificial intelligence in laparoscopic cholecystectomy: does computer vision outperform human vision?

High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in Chinese Population.

Automated detection of hippocampal sclerosis using real-world clinical MRI images