Laparoscopic Colorectal Surgery with Anatomical Recognition with Artificial Intelligence Assistance for Nerves and Dissection Layers

BACKGROUND

In digestive tract surgery, dissection of an avascular space consisting of loose connective tissue (LCT) appearing by countertraction improves oncological outcomes and reduces complications.1-3 Kumazu et al.4 described a deep learning approach that automatically segments LCT to help surgeons.4 During left colorectal surgery, lumbar splanchnic, hypogastric, and pelvic visceral nerve injuries cause sexual dysfunction and/or urinary issues.5 As nerve preservation is critical for functional preservation, the AI model Kumazu reported is named Eureka (Anaut Inc., Tokyo, Japan) and was developed to separate nerves automatically. The educative efficacy of intraoperative nerve visualization has been described.6 Artificial intelligence (AI) assisted navigation is expected to aid in the anatomical recognition of nerves and the safe dissection layers surrounding nerves in the future.

METHODS

We used Eureka as an educational tool for surgeons' training during laparoscopic colorectal surgery. The laparoscopic system used was Olympus VISERA ELITE3 (Tokyo, Japan).

RESULTS

Total mesorectal excision (TME) was safely performed with nerve preservation. No postoperative complications occurred. Automatic segmentation and highlighting of LCT in the dissected layers, lumbar splanchnic, hypogastric, and pelvic visceral nerves (S3, S4), were performed in real time.

CONCLUSIONS

In colorectal cancer surgery, the nerves are vital anatomical structures serving as landmarks for dissection. Lumbar splanchnic, hypogastric, and pelvic visceral nerve injuries (S3, S4) cause sexual dysfunction or urinary disorders.5 Nerve preservation is important for functional preservation. AI-assisted navigation methods are noninvasive, user-friendly, and expected to improve in accuracy in the future. They have the potential to develop nerve-guided TME.

Narratives of Charles Darwin Down Under

Despite the fact that Charles Darwin spent several months in Australia in the final year of his Beagle voyage that circumnavigated the globe, most studies that deal with Darwin's life or his discovery of evolution spend little time discussing his Australian period, if it is mentioned at all. His time there is largely deemed to have produced little of significance in comparison to his visits to other places such as the Galápagos Islands, which has long been mythologized as providing the key sources of observable data that ultimately led Darwin to develop his evolutionary speculations. In recent years, however, Darwin's period in Australia has received more attention, most notably a series of studies detailing the observations and connections Darwin made while in New South Wales, Tasmania, and King George Sound. While much of this literature has provided an important corrective to previous Darwin scholarship that had largely ignored Darwin's period in Australia, it has also worked to perpetuate a romantic and heroic view of scientific discovery by suggesting that Darwin's key "evolutionary revelation" was made not in the Galápagos Islands but in the Blue Mountains, a claim that has been recently made in print and online. This paper therefore examines the historical literature on Darwin Down Under, focussing in particular on this recent romantic turn that seeks to situate Australia as the key site of inspiration for Darwin's theory of evolution.

Results of the European survey on the assessment of deep molecular response in chronic phase CML patients during tyrosine kinase inhibitor therapy (EUREKA registry)

PURPOSE

The advent of tyrosine kinase inhibitor (TKI) therapies has revolutionized the treatment of chronic myeloid leukemia (CML). The European LeukemiaNet (ELN) recommends quantification of BCR-ABL1 transcripts by real-time quantitative PCR every 3 months during TKI treatment. Since a proportion of patients in deep molecular response (DMR: MR⁴, MR^4.5, MR⁵) maintain remission after treatment stop, assessment of DMR is crucial. However, systematically collected molecular data, monitored with sensitive standardized assays, are not available outside clinical trials.

METHODS

Data were collected on the standardized assessment of molecular response in the context of real-life practice. BCR-ABL1 transcript levels after > 2 years of TKI therapy were evaluated for DMR by local laboratories as well as standardized EUTOS laboratories. Since standardized molecular monitoring is a prerequisite for treatment discontinuation, central surveillance of the performance of the participating laboratories was carried out.

RESULTS

Between 2014 and 2017, 3377 peripheral blood samples from 1117 CML patients were shipped to 11 standardized reference laboratories in six European countries. BCR-ABL1 transcript types were b3a2 (41.63%), b2a2 (29.99%), b2a2/b3a2 (3.58%) and atypical (0.54%). For 23.72% of the patients, the initial transcript type had not been reported. Response levels (EUTOS laboratory) were: no MMR, n = 197 (6.51%); MMR, n = 496 (16.40%); MR⁴, n = 685 (22.64%); MR^4.5, n = 937 (30.98%); MR⁵, n = 710 (23.47%). With a Cohen's kappa coefficient of 0.708, a substantial agreement between EUTOS-certified and local laboratories was shown.

CONCLUSIONS

Multicenter DMR assessment is feasible in the context of real-life clinical practice in Europe. Information on the BCR-ABL1 transcript type at diagnosis is crucial to accurately monitor patients' molecular response during or after TKI therapy.

How to Detect Insight Moments in Problem Solving Experiments

Arguably, it is not possible to study insight moments during problem solving without being able to accurately detect when they occur (Bowden and Jung-Beeman, 2007). Despite over a century of research on the insight moment, there is surprisingly little consensus on the best way to measure them in real-time experiments. There have also been no attempts to evaluate whether the different ways of measuring insight converge. Indeed, if it turns out that the popular measures of insight diverge, then this may indicate that researchers who have used one method may have been measuring a different phenomenon to those who have used another method. We compare the strengths and weaknesses of the two most commonly cited ways of measuring insight: The feelings-of-warmth measure adapted from Metcalfe and Wiebe (1987), and the self-report measure adapted from Bowden and Jung-Beeman (2007). We find little empirical agreement between the two measures, and conclude that the self-report measure of Aha! is superior both methodologically and theoretically, and provides a better representation of what is commonly regarded as insight. We go on to describe and recommend a novel visceral measure of insight using a dynamometer as described in Creswell et al. (2016).

Scientific Research: What it Means to Me

This article gives a personal perception of the author, of what scientific research means. Citing examples from the lives of all time greats like Newton, Kelvin and Maxwell he stresses the agonies of thinking up new ideas, the urge for creativity and the pleasure one derives from the process when it is completed. He then narrates instances from his own life that proved inspirational towards his research career. In his early studenthood, his parents and maternal uncle had widened his intellectual horizons while in later life his interaction with Fred Hoyle made him take up research challenges away from the beaten path. He concludes that taking up an anti-Establishment stand in research can create many logistical difficulties, but the rewards of success are all the more pleasing.