Clinical Desire for an Artificial Intelligence–Based Surgical Assistant System: Electronic Survey–Based Study

Park, Soo Jin; Lee, Eun Ji; Kong, Seong Ho; Jeong, Chang Wook; Kim, Hee Seung

doi:10.2196/17647

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, with the universal application of artificial intelligence (AI) for disease diagnosis, some work focus on automatic cataract detection using AI technique (Patel et al, 2009;Castaneda et al, 2015;Harini and Bhanumathi, 2016;Long et al, 2017). Therefore, combining fundus images and AI-based methods is regarded as a more feasible scheme for automatic cataract detection in actual applications (Park et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Wan

Xü²,

Ma³

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Background: Cataract is the leading cause of blindness worldwide. In order to achieve large-scale cataract screening and remarkable performance, several studies have applied artificial intelligence (AI) to cataract detection based on fundus images. However, the fundus images they used are original from normal optical circumstances, which is less impractical due to the existence of poor-quality fundus images for inappropriate optical conditions in actual scenarios. Furthermore, these poor-quality images are easily mistaken as cataracts because both show fuzzy imaging characteristics, which may decline the performance of cataract detection. Therefore, we aimed to develop and validate an antiinterference AI model for rapid and efficient diagnosis based on fundus images.Materials and Methods: The datasets (including both cataract and noncataract labels) were derived from the Chinese PLA general hospital. The antiinterference AI model consisted of two AI submodules, a quality recognition model for cataract labeling and a convolutional neural networks-based model for cataract classification. The quality recognition model was performed to distinguish poor-quality images from normal-quality images and further generate the pseudo labels related to image quality for noncataract. Through this, the original binary-class label (cataract and noncataract) was adjusted to three categories (cataract, noncataract with normal-quality images, and noncataract with poor-quality images), which could be used to guide the model to distinguish cataract from suspected cataract fundus images. In the cataract classification stage, the convolutional-neural-network-based model was proposed to classify cataracts based on the label of the previous stage. The performance of the model was internally validated and externally tested in real-world settings, and the evaluation indicators included area under the receiver operating curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE).Results: In the internal and external validation, the antiinterference AI model showed robust performance in cataract diagnosis (three classifications with AUCs >91%, ACCs >84%, SENs >71%, and SPEs >89%). Compared with the model that was trained on the binary-class label, the antiinterference cataract model improved its performance by 10%.Conclusion: We proposed an efficient antiinterference AI model for cataract diagnosis, which could achieve accurate cataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Wan

Xü²,

Ma³

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany

Lünse,

Wisotzky,

Beckmann

et al. 2023

Langenbecks Arch Surg

View full text Add to dashboard Cite

Purpose The integration of artificial intelligence (AI) into surgical laparoscopy has shown promising results in recent years. This survey aims to investigate the inconveniences of current conventional laparoscopy and to evaluate the attitudes and desires of surgeons in Germany towards new AI-based laparoscopic systems. Methods A 12-item web-based questionnaire was distributed to 38 German university hospitals as well as to a Germany-wide voluntary hospital association (CLINOTEL) consisting of 66 hospitals between July and November 2022. Results A total of 202 questionnaires were completed. The majority of respondents (88.1%) stated that they needed one assistant during laparoscopy and rated the assistants’ skillfulness as “very important” (39.6%) or “important” (49.5%). The most uncomfortable aspects of conventional laparoscopy were inappropriate camera movement (73.8%) and lens condensation (73.3%). Selected features that should be included in a new laparoscopic system were simple and intuitive maneuverability (81.2%), automatic de-fogging (80.7%), and self-cleaning of camera (77.2%). Furthermore, AI-based features were improvement of camera positioning (71.3%), visualization of anatomical landmarks (67.3%), image stabilization (66.8%), and tissue damage protection (59.4%). The reason for purchasing an AI-based system was to improve patient safety (86.1%); the reasonable price was €50.000–100.000 (34.2%), and it was expected to replace the existing assistants’ workflow up to 25% (41.6%). Conclusion Simple and intuitive maneuverability with improved and image-stabilized camera guidance in combination with a lens cleaning system as well as AI-based augmentation of anatomical landmarks and tissue damage protection seem to be significant requirements for the further development of laparoscopic systems.

show abstract

Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult

Laudański

2022

JCM

View full text Add to dashboard Cite

Anesthesiology is an indispensable and ubiquitous field of modern healthcare. Advancements in delivering anesthesia result in a very low rate of anesthesia-related mortality, improved morbidity, and increased patient satisfaction. However, peri-surgical stress may trigger profound and lasting effects on the patient’s physiology, resulting in the emergence of a new allostatic balance. Such a balance may have variable effects on the patient’s recovery. However, in some individuals, the emergence of the new allostatic balance is delayed for years yet ultimately results in a less favorable outcome due to an increased risk of comorbidities. These changes are secondary to a single critical care grade insult such as significant tissue destruction, blood loss, alteration in microbiota with the possible leak of the inflammatory pathogen-associated molecular pattern (PAMP), and the induction of catabolism. Preparing patients for such stress is a novel requirement for anesthesiologists to expand their scope of practice in healthcare delivery by eliminating pre-existing stressors (smoking, obesity, substance abuse) and preconditioning patients to stress using pharmacological and nanotechnology means. Perioperative care should employ a human brain interface for more efficient care delivery and an early warning for excessive damage. Postsurgical care can involve the modification of the immune and metabolic system response. Selected techniques should include utilizing genome expression manipulation, multi-omics analysis, and predictive algorithms. Integration of these techniques into a per-operative recovery data-driven delivery system with the targeted goal of recovering presurgical homeostasis instead of allostatic recovery may represent a perfect nexus for future anesthesiologists.

show abstract

Clinical Desire for an Artificial Intelligence–Based Surgical Assistant System: Electronic Survey–Based Study

Cited by 3 publications

References 25 publications

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany

Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult

Contact Info

Product

Resources

About